{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第三章 检索陷阱\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 目录\n",
    "- [ 3.1 - 可视化技巧](#31)\n",
    "- [ 3.2 - 相似性和干扰项](#32)\n",
    "- [ 3.3 - 总结](#33)\n",
    "\n",
    "在对文档进行简单的向量检索时，我们可能会遇到一些问题或陷阱：\n",
    "- **干扰项**：简单的向量检索可能会返回和查询主题相似的文本，但不包含答案，此时这些检索结果反而会对LLM的回复形成干扰；\n",
    "- **无关结果**：如果用一个和文档主题毫不相关的查询去检索文档，嵌入模型仍然会返回检索结果，此时LLM可能会被检索结果误导，导致回复结果“牛头不对马嘴”。\n",
    "\n",
    "此时，简单的向量检索就会失效，无法提供准确有效的检索结果。\n",
    "\n",
    "接下来我们通过可视化方法来更好地观察和解释为何简单向量检索有时会失效。\n",
    "\n",
    "## 3.1 可视化技巧\n",
    "\n",
    "首先，我们来导入需要用到的库和数据："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\Anaconda\\envs\\chroma\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n",
      "d:\\Anaconda\\envs\\chroma\\lib\\site-packages\\torch\\_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  return self.fget.__get__(instance, owner)()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1028"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import chromadb.utils.embedding_functions as embedding_functions\n",
    "from helper_utils import load_chroma, word_wrap\n",
    "from chromadb.utils.embedding_functions import SentenceTransformerEmbeddingFunction\n",
    "\n",
    "## 如果报错：SSLError:(MaxRetryEror(\"SOCKSHTTPSomectionpool(host='huggingface.co', port-443)，注意挂上梯子并添加以下代码：\n",
    "os.environ['HTTPS_PROXY'] = 'http://127.0.0.1:7890' # 7890改为自己的梯子端口\n",
    "os.environ[\"HTTP_PROXY\"]  = 'http://127.0.0.1:7890'\n",
    "\n",
    "# 读取OpenAI的api key\n",
    "from dotenv import load_dotenv, find_dotenv\n",
    "_ = load_dotenv(find_dotenv()) \n",
    "openai_api_key = os.environ['OPENAI_API_KEY']\n",
    "\n",
    "# chromadb支持的嵌入函数有许多种，这里介绍常用的几种：\n",
    "# 参考资料：https://docs.trychroma.com/embeddings\n",
    "# 方式1：默认嵌入函数，需要下载模型，本地计算。英文文本表现不错，中文文本表现一般\n",
    "# embedding_function = SentenceTransformerEmbeddingFunction()\n",
    "\n",
    "# 方式2：OpenAI的嵌入函数，直接调用OpenAI的接口，无需下载模型，推荐\n",
    "embedding_function = embedding_functions.OpenAIEmbeddingFunction(\n",
    "                api_key=openai_api_key,\n",
    "                model_name=\"text-embedding-ada-002\"\n",
    "            )\n",
    "\n",
    "# 方式3：HuggingFace的嵌入函数，需要下载模型，本地计算，对网络要求高\n",
    "# embedding_function = embedding_functions.HuggingFaceEmbeddingFunction(\n",
    "#     api_key=\"hf_\",  # 填入你的 huggingface Access Token\n",
    "#     model_name=\"jinaai/jina-embeddings-v2-base-zh\"  # 指定模型\n",
    "# )\n",
    "## 中文备选模型\n",
    "# jinaai/jina-embeddings-v2-base-zh\n",
    "# GanymedeNil/text2vec-large-chinese\n",
    "# BAAI/bge-large-zh-v1.5\n",
    "# BAAI/bge-small-zh-v1.5\n",
    "\n",
    "\n",
    "# 初始化chroma，英文文档\n",
    "# chroma_collection = load_chroma(filename='./data/microsoft_annual_report_2022.pdf', \\\n",
    "#                                 collection_name='microsoft_annual_report_2022', \\\n",
    "#                                 embedding_function=embedding_function,\n",
    "#                                 langcode='en')\n",
    "\n",
    "# 初始化chroma，中文文档\n",
    "chroma_collection = load_chroma(filename='./data/2024年北京市政府工作报告.pdf', \\\n",
    "                                collection_name='beijing_annual_report_2024', \\\n",
    "                                embedding_function=embedding_function,\n",
    "                                langcode='zh')  # 注意中文文档将langcode改为'zh'\n",
    "chroma_collection.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来我们将用UMAP对嵌入（embedding）矩阵进行可视化，以更好地观察嵌入矩阵元素之间的关系。\n",
    "\n",
    "UMAP（Uniform Manifold Approximation and Projection）是一种用于降维和数据可视化的算法，类似于 PCA、t-SNE（t-distributed Stochastic Neighbor Embedding）等降维方法，相比 t-SNE 有一些改进，使其在某些情况下更加高效且能够保留数据的全局结构。\n",
    "\n",
    "安装方式：`pip install umap-learn`。注意是安装`umap-learn`而不是`umap`。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\Anaconda\\envs\\chroma\\lib\\site-packages\\umap\\umap_.py:1943: UserWarning: n_jobs value -1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
      "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n"
     ]
    }
   ],
   "source": [
    "import umap\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "\n",
    "# 获取整个数据集的嵌入\n",
    "embeddings     = chroma_collection.get(include=['embeddings'])['embeddings']\n",
    "# 定义UMAP模型\n",
    "umap_transform = umap.UMAP(random_state=0, transform_seed=0).fit(embeddings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def project_embeddings(embeddings, umap_transform):\n",
    "    \"\"\"\n",
    "    用 UMAP 将高维嵌入向量投影到二维向量空间。\n",
    "    \n",
    "    Args:\n",
    "        embeddings (list): 高维嵌入向量。\n",
    "        umap_transform (umap.UMAP): UMAP模型。\n",
    "\n",
    "    Returns:\n",
    "        umap_embeddings (np.ndarray): 二维嵌入向量矩阵。\n",
    "    \"\"\"\n",
    "    \n",
    "    umap_embeddings = np.empty((len(embeddings),2))\n",
    "    # 为保证结果可复现，逐个进行umap转换\n",
    "    for i, embedding in enumerate(tqdm(embeddings)): \n",
    "        umap_embeddings[i] = umap_transform.transform([embedding])\n",
    "\n",
    "    return umap_embeddings   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1028, 1536)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(embeddings), len(embeddings[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到`embeddings`矩阵的维度高达1536，是一个典型的高维空间，非常复杂，难以观察。\n",
    "\n",
    "因此我们用`UMAP`方法将其投影到二维平面，方便可视化。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1028/1028 [13:53<00:00,  1.23it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before UMAP: (1028, 1536)\n",
      "After UMAP: (1028, 2)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "projected_dataset_embeddings = project_embeddings(embeddings, umap_transform)\n",
    "print(f'Before UMAP: {np.array(embeddings).shape}')\n",
    "print(f'After UMAP: {projected_dataset_embeddings.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "投影到二维平面后，我们就可以通过散点图来观察不同点之间的关系了。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.8728130966424943, 5.976743510365486, 3.350202488899231, 11.059748244285583)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制散点图\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure()\n",
    "plt.scatter(projected_dataset_embeddings[:, 0], projected_dataset_embeddings[:, 1], s=5)\n",
    "plt.gca().set_aspect('equal', 'datalim')\n",
    "plt.title('Projected Embeddings')\n",
    "plt.axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "一般来说，离得越近的两个点，它们的语义相似度越高，反之越低。\n",
    "\n",
    "## 3.2 相似性和干扰项\n",
    "\n",
    "这一节我们通过几个简单的例子来探究简单向量失效的情况。\n",
    "\n",
    "首先，我们来问问北京的地区生产总值是多少。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "全市地区生产总\n",
      "值增长5.2%、约4.4万亿元\n",
      "\n",
      "数字经济增加值占地区生产总值比重达42.9%\n",
      "\n",
      "人均地区生产总值、全\n",
      "员劳动生产率、万元地区生产总值能耗水耗等多项指标保持全国省级地区最优水平\n",
      "\n",
      "提出今年全市经济社\n",
      "会发展的主要预期目标是：地区生产总值增长5%左右\n",
      "\n",
      "着力打造一批乡村产业强镇、优势特色产业集群\n",
      "\n"
     ]
    }
   ],
   "source": [
    "query = \"地区生产总值是多少？\"\n",
    "\n",
    "# 输入query，返回 n_results 个最相关的查询结果，results 是一个字典，包含了查询结果的文档和嵌入矩阵（方便后续可视化）\n",
    "results = chroma_collection.query(query_texts=query, n_results=5, include=['documents', 'embeddings'])\n",
    "\n",
    "# 检索结果\n",
    "retrieved_documents = results['documents'][0]\n",
    "\n",
    "# 查看检索结果\n",
    "for document in results['documents'][0]:\n",
    "    print(word_wrap(document))\n",
    "    print('')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到其中一个检索结果就是我们想要的答案，但其他结果并不是我们想要的。\n",
    "\n",
    "接下来我们用UMAP将查询、检索结果、原始数据都投影到二维平面，并绘制散点图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:00<00:00,  1.22it/s]\n",
      "100%|██████████| 5/5 [00:05<00:00,  1.07s/it]\n"
     ]
    }
   ],
   "source": [
    "query_embedding      = embedding_function([query])[0]  # 查询的嵌入\n",
    "retrieved_embeddings = results['embeddings'][0]   # 检索的嵌入\n",
    "\n",
    "# UMAP降维（投影到二维平面）\n",
    "projected_query_embedding      = project_embeddings([query_embedding], umap_transform)\n",
    "projected_retrieved_embeddings = project_embeddings(retrieved_embeddings, umap_transform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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u7k74vLbeQm5uLs4991w0NzfH1Yu4+uqrsWTJEnz44Yd46qmn4PP58MQTT+Css84SGlN/fz+uv/563Hfffaiursa9995rKoJaWlpwzTXX4NFHH8XSpUuFjkMQaU1KSx4RhA3OO++8aEVFRfRb3/pWdOPGjdHDhw9Ho9Fo9JFHHonm5eVFN2/enFCd7sCBA9Gf/OQn0by8vOjw8HB0/vz50R/84AfRgwcPRt96663o4cOHuf/t378/evDgwejhw4eZVe/0LFy4MHrNNdeYfm7JkiXRpqYmaxchGo1+8pOfjF5yySXRaDQafeONN6IAmFUMX3nllSiA6ODgYHR4eDg6Z86c6OrVq2Pvn3feedEf/vCHsb8BRDdu3BgdHR1N+G/jxo1R/eOjo6MjmpeXF/3mN78ZnTx5cvTJJ5+MRqPR6H333RedMmWKpXN75ZVXot/97nejs2bNih599NHR73znO9E9e/aYbjc+Ph597rnnohdeeGE0Ly8vOn369OhNN90UHRsbEzruz3/+82hOTk70scceszRugkg3yDNApC1r166Na9Kjsnz5ctTU1CTM8FWOOuoo/OQnP4kFuf3kJz/BT37yE6lj33ffffjwww9x6aWXGn5u27Zt+NWvfsV87+KLL8Yll1yCjo4OPP7441LHVxkcHMQrr7yCP/3pTwCAOXPmYMKECbjzzjvxpS99KeY2P3jwIG644QaceOKJKCwsjJ2DPm4COLJs8cEHHwA40o9B35hIfR0A3njjDUybNg0HDhzA5z73OVx22WX41a9+FcsIuOCCC1BQUIBoNIrt27cjJycnVk1wbGwMhw8fxqFDh3DSSSfh6KOPxltvvYWXXnoJmzdvxtNPP42uri7MmTMHl112Gb72ta8xx6vy7rvv4u9//zs2btyIhx9+GENDQ5g1axa+853v4Jvf/KZUX4Frr70Wl19+OaZNmya8DUGkMznRqECTc4JIQ959990EQ+bz+VBYWBhL63vzzTdx+PBh6X3PnTsXkUgE77zzjuXxTZs2DV/+8pdx+PBhtLa2cte7zdi3b19cmuLNN9+MH//4x3EVBXNzc3HCCSfg9ttv55boXblyJRYtWoT+/v5YUJ4I3/72t7F27VrMmzcPTzzxRGwsGzduxB//+Ee89NJLGBoa4tY/mDNnDgYHB3HbbbfhyiuvBACUlpaioaEB//Zv/4aGhgahTItbb70VV1xxBWbMmIGzzjoLF1xwAVasWGF5+YUgsgkSAwSRQgYGBjB58mQcd9xxqR6KLe666y6cf/75CbUTtKjegGg0ivHx8Vhch8/nw1FHHYXDhw/jueeew+LFi2PpkTJEo1G8+uqrOPHEEy0LK4LIVkgMEARBEESWwy/VRhAEQRBEVkBigCAIgiCyHBIDBEEQBJHlkBggCIIgiCyHxABBEARBZDkkBgiCIAgiyyExQBAEQRBZDokBgiAIgshySAwQBEEQRJZDYoAgCIIgshwSAwRBEASR5ZAYIAiCIIgsh8QAQRAEQWQ5JAYIgiAIIsshMUAQBEEQWQ6JAYIgCILIckgMEARBEESWk5fqARCEEyiKgnA4jIKCAvj9/lQPhyAIIq0gMUCkPa2trejo6Ij9vWTJEjQ0NKRwRARBEOkFLRMQaY2iKHFCAAA6OjqgKEqKRpTeKIqC7u5uun4EkWWQZ4BIa8LhMPd1Wi6QQ9TDkglLMplwDgThJCQGiLSmoKBA6nWCDc/DUlFREWcsM2FJJhPOgSCchpYJiLTG7/djyZIlca/V1NR4craXShe82bGNPCzafaT7kkwmnANBuAF5Boi0p6GhARUVFZ50+6ru6IGBAfT09MReT8ZsVObYIh6WTFiSyYRzIAg3IDFAZAR+v99zD3O9O1oLywXv9WMPDAwwX0+nJRlaViIINrRMQBAuEAqFuMZYhTdLtQvLFc47tqIo2LhxIzZt2mT6Oa13QSUQCHhOhBlhdVlJvU4bN26kJQUiIyHPAEE4jNGsXItTs1F9ZLyIyCgoKBAapzpG3j6Li4vlB5xijJaVWFkG+uvU3t5OQYdExkFigCAcRGRWDjgX5MiKjK+oqDA9NgDTcWrHmGnuddayEu9asq6T28s8BJFsSAwQhIOIuv7Ly8ttH8soHXDJkiVx7wUCARQXF8dmvN3d3dz9BoNBLF68OM7Qqe517T71giadc/d513L//v3cbSjokMgkSAwQhAV4ho8XZKfHCUMikg6oMnXqVFRVVcX+NprR64WAipF7Pd1z93nXsquri7tNunpFCIIFiQGCkIRn+HhBdizsGBJViEQiEeb7kUjEtIAQa6YPmC9fsNzrogWLvIzs/fBqLQuCsAqJAYKQwMjw8WaXfr8/LgLdjiHRC5HCwkIMDQ3F7dvn8zG31Xsj1Jl+X18fAKCsrIxp6M1c/5mQu88TR3oWLVqEk08+OW3OiyBEITFAZDWy69xGho83U1+8eDGWL19uez2dJUSGhobQ2NgIn88X2zcv9Y01+zWqzyDq+s+U4MKGhgYUFBRgw4YN3M/k5lI2NpGZkBggshYr69xGhXd4QsHn8zlSFMlo/9rjiwT7mSHj+nfieF4hGAzijTfe4C73hEIhhEKhtIuJIAgzSAwQWYmVdW6rhXdkZ8g8bwVvPwMDA1i/fn3sb9VQ2SnRLOv6d7IktN2sBLvbNzU1AYBh/EdHRwcKCgoQDAal908QXoTEAJGRmBkEM2PH2t6s8I4TM2QjbwVr/4FAAFu3bo3bh1bUWDXKZq5/1vVxwvthNyvBqayGpqYmVFdXo6+vD++99x62bduW8JkNGzYgHA6Th4DICEgMEBmHUbS/asCMjJ1+e7/fj+XLlwutjduZIYt4K/T7D4fDCWIAsB+8ZyRs3EojtJuV4HRWgypuFEVhigF1/wUFBXExGwSRjpAYIDIKnkHYu3dvQuc+lrFTP6/fZ0tLC3cb1hq6FaMg6poX2T8vmFEURVEwe/ZsZnCiW2mEdrMS3MpqMMs00AYcUiwBka6QGCAyCp5B0K//dnR0oLm5OWEWb1SZj7eNFjvr1TzPw8jICBRFYe6PZ6jsuLBZM3+1YJGowbVyHexmJbiZ1SCSaQCkX30FglAhMUBkFDIP/nA4jKqqqtiDW1EUjIyMSG2jxa77nGfY29vbDZvj8AyVFcNkNvPnXV+tJ4JX499MHNiNuXA7qyEYDCIcDgt1hCQxQKQbJAaIjEI0yA6IFw52Ow065T7XFgJqb28X3p9ooSEzzGb+Rp6I3t5eZmOfjo4OYZFkNyvByawGs/1HIhGmpyDd6isQBEBigMhAWAZh6tSp3BmjE50GnVyvNmpDrN2faECkDKJBkixPRH9/P/r7+02PoRc1+iUFu1kJTmQ1iO5f7ylI1/oKBEFigMhI9AbBaMbIM7y1tbWxf7NK9Wpxer3abH8sV7wTLnJRV3tvb6/UfvWoosapzASvdEyMRqMpOzZB2IHEAJE18GaMPMNrJgD0+3Zyvdpof7wlCbPgRlHMXO2Kogh5AIwoKChwbGkl2R0TtY2iktWgSVEUwx4SBGEXEgNEVqM+2CsrK+MyDqwYcqfXq3n7M1pC4AU3ymLkaucdnwfv2vIyN2SWVkQFhVOeA5HYEqcDCPXHNAomJQirkBggshb9Q7ayshIlJSW2DIbT69Ws/SWrMZBsWWQeJSUlqK6uTtiXnfNQx8bL/tAaZCeXIkRiS+zWeBA5JqUwEk5DYoDISlgP2Z6eHlRXVztaO8ANktEYyEpZ5OnTpydkQACICwx04jxEZufasslOufJFPSJOlik2OialMBJOQmKAyEpEo/+TvR4tilspdOratGxZZPX1sbExKeMuex4is3PtMWWyPMxEn8yM36mZu5GXhFIYCSchMUBkJSIuajdL7zqB00sSZjNukZmoFZEicx5GmR/5+fnCSxr6181En2gdCv1Y7d4fXl0HSmEknIbEAOFJ3HbNi7io3ap170VEZtxGRZr0ywhuXR/ZzA+R+2wm+syujUhRKztoC1EBlE1AuAOJAcITaI1/b29vUlzzZrPYZAXq2cUJ4WS2Hm5WpClZHhMrcQZm99lM9PHeDwaDWLx4sWlRKydwu5ASQZAYIFKOmQvWTUNj9JBNRqCeGWaGnpUR0dTUJH0cnsCpra1NmIm62axIBKeXInixAOo14V0bVQhYHRNBeAkSA0RKEU3X6uvrS8kD1u5D3o5BNFvH5mVE7N+/HxdddJHUuHjCZ9myZQnbinhM3Aq81I5b7aRoB54Q1Yo+UVFIs3cinSExQLiOkUFU10G9jNWHvN7QlJaWoq6uTmhfPFd8QUEBfD4fCgoKuDP0gYEBrFu3jush4BlqnvARFQ52lhFERJPTAoMnRBsbGxEMBuNeo5k/kemQGCBcxakHeFlZmZPDch2WoVEb+Yhcg7a2Nubr2uZAJSUl3O2NaiYYGWq98JEVDoB84KVI8Sc34hR44+R1gNRfG6/VnyAIO5AYIFyD9wDPy8uLrUOXlZUxC9VoScc0KqOAPJFZskjt/4GBAcP329rasHr1aqFx8fLuZYSDikzgZSgUYi51qOWLVfHhRmaHnQBRr9afIAirkBggXIP3AG9vb4+rr85yOZeXl6f1rMvMoBgZMdna/zz6+/uhKIqlvHujcaiv8+6P6Bq7SO6+Kj7cyOywGiDq9foTBGEFEgOEa5hVbFMfoDyXczo+WLWuY1axGJVkVZbTiw5W62GeAeSNY2BgAOvXr4/9zZoVi3Q+FC3iozZgkjXcIm58K7EA2VR/gsgeSAwQriBasU3NEsiESGyW67i5uRltbW1xbn+R2WdpaWncNvPmzcP06dPjuv+JYFZREQDy8/PR3d2dYAx5PQj0BXZ4s2Kjeyrj/VDPQcZwy7jxZb976VJ/giBkIDFAOI7MrC9TMHIdr169WjjYjCei3n77bbz99tvw+/2YMmUKtm3blvCZ6dOn4/3334/9XVhYKBTYpw1K1BtNffU7HrKzYp7hLCkpiYuF0AsnEcPtthvfaHmBggqJdIXEAOE4MrO+dMsS4GHmOrZqxFif4aEVAgAwNDQUFzMgMnNlGU19RUgWsrNinkGtr6+3bVCT4cZneSkoqJBIZ0gMEI4jahjSMUuAhxOuYzuBg2VlZczZu9YAsgwwbxxGKX16rN5Ho1gRO9+LZLnxteOkoEIi3SExQDiO3+9HZWUlc327pKQElZWVGedGdaJ0sVVj5ff7UV5ezhQD+n1qDXBPTw8zPVG7jVGXQBU73h03YkVSUUaaggqJdIfEAOEKavU7rSAIBAJYtWpVqobkOnar1InO3PUoigJFUVBYWIihoaHY6zwDqL7GEgKBQEAoFXF0dDR2b7VpolZxeq092RUDvRxUSHEMhAg50Wg0mupBEJlLOj6IUj3mhx9+GF1dXZa2bWxsjJUrNhp7d3d3XHqgysqVKxNq/uvXwnkte5ubmx0p2ywrLFJ9v1T056HGQKQSimMgRCHPAOEq6ZYy6IWH57HHHmt5W5/PJ9TAR2Ymq59lh8Nhphiw4hK3u9buhful4rX+BRTHQMiQm+oBEJmHoijo7u42jHz3IryHZ7LPw2wNvrKykvueqFtaXZLQYrSu7vf7UVVVBb/f76hL3KzKoRFeuV9atNcp1fD6WzhV4ZLILMgzQDiKl2ZqsjgRBOaEy9oodkB1PVdXV1sqZqRFX0NANBDQyQA9O8LCyNg5aYyN7qlXlij0GPW3MKsMSmQnJAYIx0h3t6TdGa+TQkjrco5EIglxAH6/X6qYEQ9tDQGZQECnXOJ2+gPwjJ2TQXtG99TLwtdo9r979+4kjoRIF0gMEI6R7ulVdma8bgghkXgLOzEZsmPWCw+n4kGc7A+gdsN0AqPro/6b9V6qv+uKomBkZIT7fnt7O8bGxjwjXAhvQGKAcIxUp1c54bK1OuNNRyEkM2a3Z8FO9QdQ6x848V2wEs+Q6vst2hPEK8KF8A4kBgjHSEWxFxUnjJXWgIhE5GtJtRCyguiYvbT8Y9QVUv2uOSVcrNzTVN5vXrXIj33sY3jrrbcSXk+1cCG8BYkBwlFSkV7lhLGya0BSKYSsIjpmr3g9eF0htd81J4WL2fXx2v3m3acFCxYwxYCXhSqRfEgMELZxay1ZFLvGKhQKOWJAvJZnLoLImL3g9TAy8lovjtPCxej6eO1+8+7HtGnTElpip1q4EN6DxABhCy9EVNsxVkZrrFu2bAEAqYdmuhVZAvhjFnHJJwtRI++GcDG6p1663yxPRmFhYVyL6tLSUtTV1XlmzIR3IDFAWMYra8l20tOMgq1CoRBCoVBM4Hg1p9wNRFzyyUTUyKfjco2T6FNStUIAAPr7+1FXV5ei0RFehsQAYRmvrCUDzqan6eno6MDevXvjmi55KafcaURd8slExsh7zX2fbFRvRXd3N/N9ChwkWJAYICzjhbVkLU6lp7HQt2MW8YCkqyeBJ5L6+vpSej4yRt5L7vtU4bXfJ+FtSAwQlunt7U14LZ1csqzZJq8jHwt1hsUy+nZiKWREhBuCg2cs2tvbY/9OlWeEjLw4vO+3KvboOhJaSAwQluCtt5eXl6dgNNZhzTanTp0qJBAKCgqYRr+iosJyLIWMiHAreNOoN4JKOhStSYVnxmveoIqKCuTlHXnMj46OYuvWrbHvsvpd9dJ4idRBYoCwhJfiBeyin202NDSgoKAAg4ODKCoqQjAYxK5duzA0NBT7TFFREQB2SVr14avHyM2uKAr6+vqERYTbwZtakTQyMhLnFVDx8r1ORZaLFzJrjMajp6Ojw1PjJVILiQHCEpm8Hql9iIZCIbzxxhtxQgAABgcHY93+ROG52c0e2iyja0eMic5eVZGkKApTDNi5127OoHlCKS8vz1LvApGxeiWzxmg8ZqSDt4dwDxIDhCUyNYWL9RDVBw+aUVZWhrGxMdNZWV5eHqZNm2b60GYZXatizMrs1el77fYMmieU2tvbpTozAuJjtespc1ociWbKsLZL998wYQ0SA4RleNHdoVAozsWeTsg8REdHRxNeU42k3+9HQUFBQp63FtZsm7c/PTwDDQDd3d3cpQirs1en0vXszqBFjKaZIBI9nlnXQu04nCx85YQ4ikQilrbLBM8eYQ0SA4Qt9Ovtt912W8ylHgqF0NnZiTVr1qRqeNLwHoaVlZUJHgKWx0AbQGmnb3xtba2pS1s10OpyxejoKFpaWmLv642K3dmrbCQ/y3DbGYOo0RQJgBQ5Hm+sbW1tcaV9KysrUVJSkvAdsVr4yq67nrfsVFNTg/Ly8tg96e3tzTjPHmEdEgOEY4RCoYS19aGhIYRCoTgPgdcirrXwZtzl5eVCywV9fX3w+xM758kyNjbGTVvUon+ga9EbFZ7QiUQi2LhxIwBYWlNnwTPcVmfQskZTK5SsxjvwPqMVAsARUah+N1RhYLfwVVtbG1avXm26vR6jWIFoNBon6Px+P2UTEDFIDBCO8eKLLzJfHxwcjIkBr0Vcs2C5xHnV3PS0t7djdHRUOs5Aj0jVQ5EgMe0MWKR2veyauh6RrIjCwsKEzAyrs3SjGb5q+PTxG6IzYNb1mjNnDt577z3uNj09PaiurhY2rEaCQ1EUaQNttMzFEk9Ut4FQITFAOIKiKBgeHma+p6bhuRVx7bSngbU/O9UKzSgrK2NmJphVPRSJb9CP26x2Pes4oohkRQBgZmaYGT47a/J24h3UbdWlASMhoCIThOf3+xM6ClrZj4pZrAAFCBI8clM9ACIz4BmmGTNmxLwCRrM7q7S2tqKlpQXr169HS0sLWltbLe/LaH/qLFHLvHnzbB0LOBIbUFtbK/x57bUyM4RGwYdVVVXw+XxCxxFBxEtRUFBg6BY3gnX9A4GAlNGtqqqybAhZxpqHbBAer3GQ7H5aW1sNA1at7JPIHkgMEI7Ae8g0NTXF/j0wMCC1rRk8T4OiKK7sr6GhAY2NjQgGgygpKcHbb79t6Tha1DX6ysrKuNcDgQDz89prxTKQpaWlWLlyJZqbm1FfX294bKPrLntPzMSDKkzM3OJGNDQ0xF2nrVu32hZ/IvDqSSxcuJB538LhsNR3kHUfZYP5RMSYUwGCiqKgu7vb8u+M8Ca0TEA4glkuuqIoTPe5zOxOi6Io2LJlC/M9q65Qs3Vp0aDAmpoavP766wnucD3qube2tsZdm5KSEhQXFyMajZpGpzc0NMTFFvT392P27NlC3QV5UfdWjAbPyOuzIuy4xVnfoVQWypkzZw6WLVuG6upqhMNhDAwMJJT7FY29sJu6yfvu1tbWIj8/37ElNP1voLKyMk7wezk4mDCGxADhGEYPNN7Dqri42HS/+geMmVG26mkwWpcWmXktXLgQS5cujZ33ww8/jK6uLu7ni4uLmfsdGBiIeVHMotPtGkh9eqLVbAKeGFy2bFnCZ+vq6phiwOy+paoEdllZGTMjoaysDMBHDX/Wr18f977+PpgZSjvBfDyvm1PZIbzA0J6eHoyOjmL58uUJmS16oUB4GxIDhKPwHmhOVcxj5ftrseMKNfJuiGQTaIUAAJx88smGYsBoDV3FLDrdCQNpN6JcNXIVFRVCs1urFQ1TVQJbZLyyXiUns2h4XjenBJKZ+FYUJa6+hYo6JhIE6QGJASIpWDEAMqWBg8EgFi9ebPsByPNuiBic3t7ehLQtnnhR15ZFKsUZGfZU94iwauSsuMWtiggnXNdm45X1Kjm5vMETIqqRtpsqaqdehmyqJZE6SAwQrqEvSyxrAGQi2ouKihzr086aKYtUtdM3w2HFAlRWViasLevz7vUMDAxwYwCsGkgnsGvkrHgkZL5DiqIkVAsUNYx6ASHi4pf1Ktld3lDHtGvXLsPP2REedjJ9tPsgMeB9SAwQrsArSyxjAGRmt9qUKrcKGYm09VWb4bA8AgMDA/j4xz+e8PrQ0BCWLl2KTZs2MY/Lm13JuuetEo1G8YLyAp7sfxJ7Du7BMUcdg7NKzsIxo8cwP68GdrplAES+QzzXtohh1G+rF2u875esV8mO90a2wqVVg+yEh8lqnwQiuVBqIWEbfaqRUVliGVgpV2rQlhF20gtFxlRVVWU6Dt5yBq9K4/79+xPS1LRs2bIl7pz09RB6e3ul8uhZ92zDhg0J92jDtg1YfOti1Nxeg1tevgWtO1pxy0u3oPbOWpz/7PnoRW/CvkOhkCM1H6xi5to2mu2yttV/l42+X6x6Bk6kDpqN0QyrRp039ubmZpSWlgrtw6ieBeEdyDNA2IK1Zsx72GrLEouin20B/LxvLW67JkWWDVjwqjSqRrikpIQZGR4KhRAKhTB79myccMIJttzz+ns2ZcoU7Nu3L3Yc1Ytz68u34srHrsSnij+FJ1c/iYaSBjzzt2ewqWMTXsfreGHvC3gAD+DT+DROw2kJx0lV2p+Za9vIMIq6xVnfL6OlBKe6PsqMUUUVHlZjJ3hjX716dWyfAwMDXAFMhY7SAxIDhGV4a8Y81LLEsujdwiJGOBkPILNmOOoDWA8vzx44spRglDExPDzMFRQiAoh1z1QhoDI0NITbnr4NX/3HV3HVqVfhd5/+HXJzcmPb5iIXJShBMYrxNJ7GE3gCJ84+EVOHpyYcz+0lAxZG995sRi76vdF/TiSQkrW8wTPQRoabN8bGxkb4fL7Y+0bpuLJLabylGfX1qqoqVFdXJ8RoAImBtYQ3oWUCwjKyM5TZs2ebfsaoupn6ntlsM5mtWP1+P5YtW8Z0pS5fvpy5TUVFheE+Z8yYgebmZmkvioghE71n//Pq/+Dj+R/Hlwu/jKHBI25yfcngHOSgAQ0oQhHWDa9j7sfpJQOR6ne85SWRqoysbQsLC+P+1n+/rFbC5JW+NiuxzXPdB4PB2BKFdrnC6UqdPPx+P7O0spvLdoRzkGeAsIzs7LutrQ11dXXcGY/R7EU0YEotF5xseK5UVoS52Rpqe3s7xsbGsHjxYuE4C1EBJHLPRjGKzj2daEQjHnn4EQBH6juwvBm5yEU1qvEQHsIu7MJMzGTu04klA5nZrRPNiUSzCazUeuAZ6IKCAqElIJnzS2axplQVhiLsQ2KAsIzsunl/fz83zcsoTU39txllZWUpEQIqLFcqz7CYoRoGI6qqqnD88cdLGTvWPdPGDADA8IRhRA9HUYGPPBhGhZ4qUIGH8BAUKJiJmZg/fz7efPPNhM/JGAR9WqqVNEY7xZT02xrti3efjFJCeUaTF2Da19fHTHcVOT9eNL8bUf7JPBbhLCQGCFs0NDSgr68PO3fulN5Wm5fvREdDme5/yYRlWERE1Pbt2w3fnzZtmlAPAj0sgaIa38mTJ2PLpiPr/EfhKKH9TcAE5CAHYxgDAOTlsR8rop4kVlpqdXU187NemHHyiksZFdzhXQtePIjqLbKSMtvbm5jxAbgT5b97926p1wnvQDEDhGUURcHDDz9sSQiotLe3o6WlxbCjoYgRsdrwKFU0NDSgubnZUMBs27ZNaF/adXTRjnL6FLhgMIjGxkbMnDkTkzAJAPA+3jc9dklJCfZgD6KIxrYbGBhASUlJ3OdqamoAwHRsvLRUXmEdr0Sq689XhSdmraTNWll7VxSFG6zqxrXbs2eP1OuEdyDPACENq7KbXXp6ehJmV9p1cNba+549e2Kf37p1K6ZOnepKsSG38Pv9tiq8lZWVGcZSWCm+VFBQgGIUYyImYgu2oB7GAXc5OTkIIYQJmIASfGQQ1awItclSb29vXP36kpISTJs2LbYMoDI4OMg8zs6dOxOyMJIZKGqGlcJCWg9NJBLB7t27TdNmZT0hvO+XUw2M9Bx77LFSrxPegcQAIYRIPrFdSkpKYu1g9evgrHoD+uYoqWxnaxWrs7NAIIDh4WHT8shG14MVFOf3+3HGkjPwTMcz6EQnTsNpOAbsSoMA0NXfhZfxMqpQhaNxdNx7qptcHYsW1ROkrWsAHEk/ZQVNar0kpaWlqKur89R9tloW2u/3J3T7M0L2+2LUWtoNzDo8Et6FxABhimzpU6uoRkkkIMyteu/pgra3gRG862EUmV9RUYHLd1+Or7/6ddyNu7EaqzEN0xL2sQd7cC/uRQ5ycDpO5x7fDLU6ZTAYRDAYRGdnp2Gvhv7+fmYKm1M4XZzH7Fis31ZtbS1GR0e5njJRrIoUqyT7eIRzkBggDLHbtUwU2QcGb8YzMjICRVHS5uHjRCMYI1jXySgyXztL/RK+hLtxN27GzTgJJyGAAKZgCvZjP7ZiK3rQg0mYhC/ii0yxwDs+C211yjVr1sQCGsfHx5ltoN0SfW4V5+HBu//5+flYtmwZqqurY0sHVmfXTlY/tEI0Gk3q8QhrkBggDJHtHMhb82URCARQXFxs6QHFi8hXGwW51azIadwMgOMJLN497evri7ueszALV+AKdKITL+NldKEr9t6xOBan43ScglMwBVOY+9MGdZp1ZtRXp1S9BIqiMMWAG9fN7VbDLMxiDbTizM73Wqb6oR1ScQ0JZyAxQBhi9tCdN28ejj/+eEybNi2uc6AZtbW1WLZsma2xGZUDTpcHEC8tzQqNjY2YPXu26QNexpBOxmScjtNRgxp8bPHH8PHFH0dvdy/e6HwDPhinphUXFwM4YiDMhACvPkQy3c6pKJhjdH5uGla7HhAeVHQofSExQBjCelipM/pIJBKrhS7r7nYqoMjv93MjsNPhAdTa2upYQGY4HEYwGIydM2/mxzNAvOCv2trauOjzeTnz0NLZkvA5PWaNpebPn49AIGBaKKqhoQEFBQVxRYjcwGiW7sYsWkWfVeDz+WLHY2H3e+2myHCjXTORHEgMEKaw1hz1Mwuj9rt6nJzZGQU3yjyA3HzYGx3TajzGrFmzEuo7aB/oZjM/mfLJeg+OjDfD6P7U19cLXWvtPkKhEMLhsCtLQDyRpI/2d2MJipVVwPtN2TWsbs7eKYAwfSExQAihXXNkGTFenYD8/HwMDg7GZjxOzuyMjKnMA8gtl6kZ+sY/MlRUVDCLPakPepGZn2j5ZBZNTU0AjMsU62MQtIjeHzdmsTKthoHkpLDK/Ka8PntPdcAiYQ0SA4Q0vJnFgQMH4v6ORqMIBoMIh8OuzOx445CJR0hVwJNRZTg9rGI706bxo/ftzvxEI+Kbmppi0e6s5QUevPvDMtJOz2JZwk+/BJGKFFbeeaodLJ00rKzZu+iynagHzU5fCCI1kBggpOHNIPTGTaYLm5PjkIlHSFXAk0yMRV1dXVy3x97eXmawptms0Y11W/WhPzY2JhyDwLo/PO+Mk7NYnvBT0RdAMjqO09eStz87PQmMUGfvaiXRvr4+9PX1GXrFUuVBI5ID9SYgpJGpq85LNXQiv541DqfqFbgd8CS6fzU9T+0lALA7ODY2NqK+/kjpYCeuiyxqr4WVK1eiubk5Fg9gNg5FUbBx40amkVbrRTh1LjIFkFSeeOKJhM+4cS1Z56lipSeBKCwBzzoWT0i5NS4i+ZBnIMsxcvuZra0WFBRg+/btmDRpEubNm8eMGueVl/XK+mSqAp5Yx9Xn4gcCAaxatSpuO55B03egS8W6rWwMgllly02bNmHp0qWOnYtsASRW0yTgSEEgN2hoaEBeXh7Tm+KGp0rGK8aLb0mHjB1CDBIDWYyR28/MJah/v6urK8GY1dTUJMQMqK87+QCxuz6ZqoAn1nHN1mRlPBleWbflFbwxy6TYtm0btm3bFvvuuRHpzkItgMTzammrJToNb2nFKfGs/X6JfpeS3fmQSA0kBrIUo8A59d+s93jFUIAjLtbGxsZY7QH14Z0O0cWpMpz645qNI1NSt2SWiZwM6FQ9WrwCWdpsF55XS18tUY+dNFU37y9L4IscK9mdD4nUQGIgSzFyERpt4/cbt931+XyxtW0tXpmlpqKegNOkg7gyQ3ZG6aQ7Wr+korJo0SKcd955sb9ZTZPMUmOdCLJz4/7yxH9zc7PpsZLd+ZBIDSQGshQrgXPqe2r7Wdntk43e8GdSNHQyxZUbAopX2XL69OmuusmN9nXyyScnvKZtmmQmBJxMU3X6/hqJ/6qqqqzwRhHGkBjIEvQPdLMfuFG9dF6hGS89IFgVEvXjTpf+BanETQHFi5mQqVlgBVnjpjZNMiMVaaqiQs1u1gzPW5EJnjbiCCQGsgDeA93IHcl7z4lCP27Dq+bGgqKh+SSjIJN+Bpwsg+qGKz7ZaaoyQs2J2b3+XmWSp40gMZDxmD3QjdyRrPcikQjzs7yKeNpxJGsGIROc5qVlDa+RipluMg2q0654uwZX5jdiRag5KYCoVXHmQWIgw3H6gc4LvuK9DiR/BsEzHG7Uec9kUlGQKd3Xp60aXNnfiNXftVMCiFoVZx4kBjIcpx/osvtLxQyCZ1Dq6+tRXV1Na5yCpMowW6m/4CVkDa6V30iqWwWn+viE85AYyHCcfqDL7i9VMwjeDM0rKY4yiEazu4EX0hgzfW3aym8k1R6UVB+fcJ6caDQaTfUgCPdxemYluj9FURJawAJAc3MzPTgEuO222+Ly3AsLC+Ma6aik08zZDLMyxYDc98fr18bObyTV55bq4xPOQWKAcB39w1112RPGhEIhZqW8xsbGOA9BJs2ceYZRz8qVK5nFrfSky7Wh3wiRamiZgHAdL7ia0wl1trV9+3bm+9ra+JkW1S2aCcLLatGSTteGfiNEqiExQCQFL63Ve9m1KeIi19bGT+eobtZ9EA1A27BhA8LhsCsR96nCS78RIvsgMUAkBa8YYC+7jUU6+emDCNM1qpt3H3iBafn5+QlLJl6PuCeIdILEAOE6XjHAXncb82ayixYtQm5uLjObIB2jus3uA8tlvnHjRua+tLN82ZLbXsIrYpnIXkgMZAGpfNB4yQB73W1s1EDHaHzptt4sch+05a83b97MLSetXjMrJbe9glfEMpHdkBjIcFL9oPGSARZxG+uFUzKFlN/vR2FhYUIqochx02m92ew+KIqCtrY29Pf3G+5H2zzLasltPcm834qioK+vzzNimchuSAxkMF6YlXtp3dbMbawXTnrD7LaQUhQl7ngAMDQ0BEVRMsowGN0HkQBKIL4xlqjgNDP0yRTOZufpFW8VkT2QGMhgvDAr99q6rVErVv3DWW+Y3RZSXrhfyYJXclhECABAWVlZ7N9GglMVAAMDA3FLDXpDzxPOeXl5KCsrc/T6i5wnBTkSyYbEQIaiKApGRkaY7yX7QeO1dVvVbawoCrq7u1FQUCCc354pHfu8gDYuAAD6+vqEttOLSZ7g7O3t5RpdvbDj3f/29na0t7fD7/dj+fLl0j0HWN95s++aV4McicyGxEAGIupqTSZeW9PWX6PKykqh7ahjn3Po78GsWbNMt1m6dCk+9alPJbyuF5wATCsZaoWd2X1VKyOKLh0YLTnwjlVbW+u4F4IgRCExkGGIuCDb2tqwevXqJI3IO6gztUgkknCNenp6Elocz5w5E7t27Yr9naqOfZkI63u6c+dO0+02bdqE8fFxpkHWCs7u7m7TfWmNMkuIsRBZKhIJaGSJPjUGQt1Hpn8HCG9BYiDDEHF39/f3Z1xQmhki3pKSkhIAiAmCXbt2obKyEiUlJUl9KHvNi2KHsfExHI4cxtF5RyNn40bsvf9+vP6VryC8b1/CZ32HD+NTzz6L7WVleKO4mLtPEYNsNtNnCTtViPX19aG9vZ27rdlSkUjsh5HoS3UGEJGdkBjIMETd2JkYlMZDNDAtEokk5LP39PSguro6JfUZZGaGXppJ7v5wN+7eejdu7bwVrwy/AgCY5puCz7+4H1/dHMWkZ5/F85/7HDBhQmwb3+HDuPD++1E2MIDqF1/EvV/4AnaUlnKPYfb9Zc2+A4EAiouLDa+R+vqOHTugKArzM2a/MdHYD73oo1RDIpWQGMgwRN2dmRqUxkLEW1JTUwOfz8fdPpkPYtmZoZdmkh1vd+C8+8/D6IFRrKpYhatPuxoTe7ej986bcEdVFLecAlzTMYA199+HtZ/7PCITJsSEQMmOHQCAnPFxfOHeew0FAe/72/1uN+7suhNv73kbuTm5KFxUiOWzl6NyXqXQPTTzIOk9CiwRZiX2g1INiVRDYiAD0bogI5EIent74wq4ZHJQGgue4WhsbITP50MkEon9X2Z7N5CtDeGFWhIqoXdCOOvus3By4cm4t+leFB1bBDz9NHDplYiORfGjZ4GbTwO+dRYwnrMDV95/P975zW+w4LvfhX/HDuT+q5t6LoBxA0HA+v727uzFZY9ehk1vbcJxU49D5ZxKRMYjeGrgKfy+6/dYWb4St664FbOm8IMUeR6kRYsW4dhjj00I7jMSYQ0NDSgoKMDg4CCzjLTIcbVkk3gnUgOJgQxFmzJVV1eHuro6z7iRkw1vphYMBk0LDSVbOMnWGvBKbYJoNIo1G9Zg4cyFeGL1E5g8YTLw7LPAihVAJIKcaBQTosA3XwB848DVnwY+1zOAJatXI/eDD2JCQEUrCO656CJUfv3r8Pl8zO9v97vdOOOuM3Dc1OPw18/+FY0nNGKC78gSxL5D+3DfK/fhumevw5Lbl2DTpZswZ+ocAImzet61XLBgAaqqquJeMxNh2u9VKBQy7LBollKZbeKdSA0kBjIUVupcU1NTCkeUWkSL3AwNDcU8BqkQTrK1BrxSm2Dz4GaE3gnhsS88dkQIAMBjjwGHDyd89t83A7/+JHDLqUDLIx8gRycEVHIBYHwcS0ZHUcaZWR8cO4jG+xpRPKMYz3zpGUw/enrc+1OOmoKvBL+CM48/EzW312D1utX425f+xpzVV1RUMI/BupY84dDX1+fYuv/ChQuxdOlSEgJEUshN9QAI52EZuZ6eHqxbty5FI/IGfr8fVVVVpoVment74z4ni1rMiBeAZjbGJUuWxL2WDjPDu7fejfnT5mN5yfLYa8+ceSb6SkownpMT91lfFLisE7jvJGAshy0EAGA8JwfvLl6Msgcf5H7mL//8C97e8zbuWXVPnBDQ34PiGcX4w6f/gGdefwZPdz/NNNYAhK89T2ypRYpY8L5v2mqKWmSEgJ3vHEEA5BnISHhux1RFxnsV3gPdTuqlE8F8MrUGzJYJkpVlMLR3CBWzjsysH93+KG554RZ0vdEF3+ocVA9MxTXP7cVpg4AqC04aBj6cAIxOAmYnZhkimpuLg6efjrlPPgkcfTT3uLd03oJPHf+p2LGBxHtQWlqKuro6nFd+HgqPKcSfu/6ME3Fiwr5UV77ItWc1lTKD932zW2zKSwGkRPpCYiDLoKjkj/D7/SgtLWV2x7NynZwM5hOtNWC0TJBMI5GXm4dd+3ah9OZSvPH+GyifVo5ZmIVobhR/K92Lv5QBpynAfX8Bjn8fOPyvxI0JrJjN3FzknHUWJj30kKEQAICud7vww7ofxv5m3YP+/n709/cfOf/iBvQM9TDFgHotRa49q6mUGU888QTWrFnDfM9qsSkvBZAS6Q0tE2QgPLcjQFHJeurq6pivW7lORrN0t+AtKwBgGgm33MiTJ0zGy++8jMJjCvHiV15E6/mtWImVWIVV+FrO1/GlQ+dj55QcLGkG3pgO/K0YKNwDTDuo21FuLnDsscD995sKAQA4FDmEo/M++pzRte7o6EDkYATIE18O4GHlng4NDSEUCnHf1y5jibr9U/GdIzIT8gxkILxaA+mw9pxsnOwHkKpgPtaskleO1w3P0J6De7D+tfXIQQ4uPPFCVBdVA0Dsuk44HMH3H9iCY96LYumXgQvOB3pnA9c+D+TqQwbGx4E9e4DPfQ4Q8AzMnToXr+16Lfb3wMCA4ed7d/Zibv5c22Wfrd7TwcHBWJohbwlHxqPjlQBSIv0hMZChaEurAsiqBiiy6+RO9QNIRaMh7blq09+SaSTu2XoPPjj0ARoXNuJHz/0IgdkBTPBNwJ4Ze1CxuBif+n83I//115EzDvz6SeC8LwATx4Cv8CbJ4+NH6hOsWmUqCL5w0hfwp5f/hJ/X/xyjw6MJFSS17MROdIY7cU/tPQDslX1m3euioiIMDg4abldUVASAb/Bl3f7Z1tyKcI+caJST10MQaYgXgqmSFbRndq7692tqalBfX+/4OBbdsggl+SX4YuCL+NJDX8LeQ3vj3v/UDuCqzcBxe4Hvnwk8UwzU7wBa7zbZcW4ucNZZhoLgjfffQPHvivGD2h+gKb8J69evZ35uHOPYWLARrx14DW9/421MzJto4UwT0Ta/2rBhg+nn1RRGVkfFpUuXYv/+/cylhJUrVybUOmCNIxvriBDOQJ6BNCNTfvRunIdXgqmS0WhI5FyT0QExGo2id2cvZk2ehVUPrMKJs04ExoFXw69i8pgPFcMRdM0Fmj535PPHjwL1A8CBo/61g9zcI54AFuPjwJNPAtddB/zqV8yPLJi+ADeccQOuf+56fHjyh5iIifAhvqz0qTWn4q7hu7CpbxPWXrDWMSEAfHSvRbokAkfuUV4e+7G7adMm7nZmHp1Mam5FpAYSA2kEL2Uq3R4Cbs3evVKNLxmInmsyjMRYdAx/e/1v+PVZv8bVn7gaW7duxR3r70D/3mcwtv8VTPsQ+DAP+IcfOGc7sP8o4J+z8FGw4J49bEGQmwv4fMC55xoe//u138fY+Bh+3P5jzDxqJk46dBLmYi6iiOLgcQfxm5d/g/2H9+N/V/0vPvPxz7hyDdJtjT5TJhWEc5AYSBNMU6ZsGNNkPhjcnL1nUzCVV8515MAIxqPjCMwJ4Buf/EZsDDMwA6fOOB/F4UX4wj33Imd8HL//BPCNs4GPvwd8PIwjSwD3338kWPDpp+MFgSoEHn0UOPNMwzHk5OTghmU3YFXFKvzppT/h7q1348DYAQDAzN0zceUpV+LyUy7HgukLXLoK/C6JW7duTfhsWVkZxsbGhDppqnR2dgKA7d+poihoa2uLS6elugQEQGIgbTBLmbJqTJO9xu7E7J0nXrIpmMor53pH1x3w5fjQH+7HnoN7cOzEY+H3+1FZWYmenh7sKC3FvV/4Albffz+u6Izgh2dE8c85wC9Ggx/FAjz00JFgQVUQaIXAWWcJj2XR3EW4tfFW/OGcP2DkwAh8uT7MOHoGfLnsbpROw1qWmTp1KvceyYiBrq4udHV1xf628jvldUakugQEQGIgbeB11FNxq0iO014D3sx1ZGREqOqfmXhJxjq5V0j1uSqKgns670Gdvw7tg+34/rPfx+8//Xu0trbGRfUfOuMMhM8+G9OaL8Exh8aw/6gcnN3S/lFQoFYQPPkkxnNzsfn738dRM2eC3+svcSza66A2I0o2+mUZ3j2yWwdA1oCbdUbMxKU0Qg4SA2mCz2c8u3G6SI6+8xrgjNeAVwNBreludAzRJYZsCqZK1bmq3w0FCiaNTMIVx1+BmzffjA8++ABz/jkHR+Oj6H9FUfBf2IOO64rxbqQPk46aDN/kKfE7/Jcg2Ppv/4Ytxx2HN6JRYMMGdHZ2JlTt0xt+L2SQGMG6R04s58gYcDPxkYlLaYQcJAbSBKMfq9NFciKRiKtr+9oaCPqmLkbHyKYAQS+j/W5MwAQcxEHMfH0m/t8n/x9+9I8fIQc5CCCAOZiDKKJ4A2/gNbyGieMTcV7pKrzw3gvM/Yb++U9s+Ff1RBW1ap9aqIfVjVNfWyAd3N48USyDjAF34/lBZBZUjjhNUBujaJk+fTqam5st546zStkCwIYNG9DW1sbcxqkyp36/H/n5+VLHsBI0J9vNzU73t2zpHKe9Px/Dx7Ad2zGGMayYswIdF3agBjUYwACewBN4Gk9jBCP4ND6Nr+Pr6BruQs3Hapj75RXsefHFFwHwu3GajdGrNDQ0oLm5GStXrkRJSYnUtrIGnPVbLysrs/X8IDIL8gykCazGKO+//77t/TY0NKCgoCChYAqreQ9gzZ3IizvgxUE41d1N1n1sx93sdVe1k2jvzyk4BZuxGa/iVVxecDn8fj++t+R76OjoQBRH6pnl/KtX4QAGsGPvDvz5lD8z91tUVMQsuDM8PBz7DlkZox6rcTBuZN2o+2GVUVZ7FKgEAgEUFxdbPn6qY0wIb0NiIE3gPQj7+vps/6h58Qj6jn5W3Ik8I8mLbDY7hugDTXaZw86yiFeKHSULrSibjdkoRzme8j2Fd3PfhR/+2D3SprCFEcZjEx7DJ+Z8AmcsOIO532AwiI0bN+KDDz5IeK+vr4/bgEu/VOCkQLS7nQi83/Ypp5yC5cuXJ3zXVQ+U/jURI59N8TSEHCQG0gTeLLq9vR1jY2O2Hky8WVRdXR3q6uoszyR4RrKgoIApBBobG2Nrw0aIPNBk4wvsxCNkYyyDVpRdeMyFuOTZS3DGnWfg36v/HZeffDmO9x+P1atXo3N7J27fcjvuff1ezD1mLtZfuB45OTnc/QaDwYQ4EhWeZ6i+vh7V1dWOC0S724litPyl/66zRIk6Hu1rmeqVItyDxECaYJRNYOfBpM4o9LOrQCDAbIAjA89I8taGzTImZNi1a5fU63aK+HilAFCyUQ2Voij47eLfouX1Ftzy8i34xfO/wLxp85CDHCh7FEzMm4jVlavx8/qfY8akGYb7LCsrY4oB1SvA8wy5IRDtbieKyPKXoijo6+tjihI9meyVItyDxECaYGZY9A8mEbchKzK7pKQEAwMD2Lp1a6x6mvq6rFuS583grQ07aTz3798v9bqdIj5eKQCUCrTfIT/8+N/T/hejc0fRP9KP8eg45k+fj8+e+FlMP3q60P5ErqVVV7dV0ZYMsWe0/MVbUjMik71ShDuQGEgTzFKRtA8mkfVNXmT2ggULEiK0e3p6Yq+JuiWNHmDhcNh148kTHGoLWRZ2AqwyMTjLTPCxvkNbXtyC5uZmXLzoYsvHdeta8koGqzN/3nGSJfZYIsesWBCPTPdKEc5DYiCNYAVmAfEPJtH1TVkXvnZfrNf0VQuNHmAdHR1obm42fODbjdwOBoPo7OyMy8AoKioyjUmwE2CVScFZRoIyFAphcHAQ45xug07MSt26lhUVFbGugaOjo3EeMKO19lSJvb6+PsP3a2pqEI1GHRcqMr8/anqUGZAYSDP8fj9Wr17N/QHyHh76BzRv5sCbUZuh3b9IClg4HEZVVRXz4eFU5PaaNWtihktECBBHMBKUTzzxREKKq55UzEqtLIvpMVtrtyJQ3DKUCxcuxNKlS2P7dFKoyPz+simlNtMhMZCmsB5MRg87/QOa5/oMBoMIh8PSrknt/u0E3TkduR0MBkkESMITc3pPC4/e3t6kzhCtLouxcHKt3WxcIkKBF1C5dOlSAIhLMXRi3DK/v2xLqc10SAxkCEYPO57bkOf61L4+MDCQkMNt5pYUKbXKMxjZmKbnNrKzU55QO3DggNDxkmkQ7C6L6TFrCObUuERn1DzR3tvb68qMXOb3R7/VzILEQIbA+2HW1tZi2bJl3O30rn19qlZVVRUzh9vMLakVFD09PQkV1ngGI1vT9NzCihuXZ4Dy8/Oxbds2oeM6ZRDMhIxRMS7tdqLfnw0bNiAcDts2rEaGEkiMvTESUHrRDgAtLS3C28sg8/uj32pmQWIgQ+D9AHlV21REjAXLBSnillTfZ5VaBdgGIxmR29kS8GTHjcvzGumXCmbNmoWdO3cmbO+EQRD5bvKOo3Wtq9vxBI6+FLcThtXIUFqZUWt/b93d3dLbiyLz+8vmlNpMhMRAhmDlh5mMNT8j9yzvgelm5DbPwGSiQLDqxtVeC33BKVZQpv6aOhXNLtqu2mxJSt2O9b1yy7Ba+T2KCijeUoZTSxwyv79MTKnNVkgMZBCyP0wZY2HVWPIecIFAwHA/bqSW8QzM3r174+IiMiUi2oob12g2rn4HZs+eHReU6YZBkPluatMFATAD7tTt9N8rN13dRtUS7cyoeZU6nazgKfP7y6SU2myGxECGIfPDNHoQqg/+Xbt2Yfv27RgeHo69L2MseYVeVq1aJbS9k/AMjL7IUqZERMsaHaPZuFnAmtMGQdRIs6poyuyPd40AJDQDsgLruiiKgtmzZ6OxsRE+n88xgc16PRM9XoQ7kBjIYkQjlfUYpRqxHjxmM0cnHlgi+5CZ7aU6Itqph7jMrN0oGC/ZKWQiQoZXRVOmiyGQeI16e3vjAvSWLFliy/OhvZcsUSXb+0NU5MkEj6pjjEQilgQKkf6QGMhy9A/C4eHhhIAqFvrWyalsDWsnTYtHKiOirV4TnoAQnbXLnrPbgslMyPDEy4wZM1BbWwvgSACtyBjVa8Tzjlj9jtotdGQmsNUiY/pAYZl4IN4YM2W5jBCDxAARexDKNETRtk62mlPtRACj7D4aGhpQUFBgKHhYM9BkuVqtXhMnRBVvxskrfJMMwWQkZEQyCcbGxqTumUg9AlEDHolEbBU6MrunWi9De3t7nAdjZGRE6FhG9UkyZbmMEIPEAAHAWkMU9WFhNadaJEjMap650ayVF2gVDAaxePHi2AyRVXTJ7dmSlfMJhUKOufF5s3EvppDJZBLYjaPRo94n/XWy0mFQf0yjdsXquYh4MESOZSZ+Ur1cRiQPEgMEAPOGKDy0hVD0mOVUmwVCiaQBWokG572nCgGjB7rbsyXZ8zHrDmllnKzZOE8kpDpATTuukZERw0wCEUSXkgYGBrB+/frY3+qsXFYI6EWVmZhQl+dEKyoaHQuw3r6ZyDxIDBC2UI2AlZxqv9/PDfaSSQN0ata6efPm2HGMcHO2JHMtzbw5TuWda8fmRJyI02jX+2WWM0QDXvVBf4FAINbpUKWjoyMuvZFHTU0NysvLmccV8c6py3MVFRWmxwKOVCDNz8+PXQN9hoSR+PGC94dIHiQGCAD8hihGaB8WsjnVAPB///d/ca2YS0pKUF9fD0AuDdCsHbIeo32L5Gq7PVsSzQAwmx06mXeuJ52a1AwPD0tH2muFj9/vj7sf4XA4QQwYwUohlFny0qNeZxEPhhpAaXS+2u8bZRNkLyQGCABykfbz5s3DWWedlfCw4AV7maVuqQwMDGDdunVoamqSTgPktUNmYbTvrq4uw22TNVsSyQBIpYvXjSY1MksOrM/yxqTvN2BFyIjcj7KyMoyNjSUIX9GumbLfeTMPhpmXTXu+vHoIVKMgeyAxQMRQHy5btmxBKBRifsZqwSCj1C0tPT09qK6uht/vx8yZM7Fr167Ye/q/VWSNHmt5wohAIIDi4mLPPRRT6eJ1unKfzJID77NGyyJa48eLjxEVMkbLW3ovgsw9kPleqtfZyINhJpKMzpdVzKmpqUn4XIj0g8QAEYff78fw8DBTDDQ2NgrPcvSoswxeypOWcDiMzZs3Jxj+Xbt2SReU4aE+2MwevGZdH1NNqly8VuJEeMjM1EVjSViEw2HDglqiQqa1tTXuWJWVlbHlLcBeNcaSkhLT8zC6zqxjmwk3vQeAV8wJAAmCDIbEABEHL5pZxt0puk8ekUiE+0AsKSlhtlS2QlNTExYsWIDBwUFMnjwZmzZtSviMWddHL2DH+FjFbklddR9GAlFf2AoQjyVhYZT3LypkeIZS9WZZRVubgIWd62wk3FheltmzZzP3wztPWk7IDEgMEDF4LvylS5di5syZUBTF0gNfRggEAgEMDg5y31cfOE48dPQPwsLCwrj2vIFAIGZ8rM54M/EhyTIgsiV1RQSitrCVitVliJqaGm5ApYz3x41YCbPvYVlZGWbPni28f9b3jhWUyvOyNDY2cvetP0+vZJQQ9iExQMTgPei0M2bZHztvn/qUJ7XAj1GUtlmnQzPU1ruTJ09Gbm5uwoNwaGgoNgNTx6KOR/a8M+UhKeJCdqJqJA9WoBur8RXre6OfTSuKwjyGjPfH6VgJ1rVQv4e9vb3o7+9HX18f+vr6hL5DRt87vYjm/TZ9Ph83dkF7numUUUKYQ2KAiCHyQNP/2NVqaQC7Djxvn6zPaou46LESuKg1ZE888UTcbIuHajz0Y5F5yKXjQ5I1m5RxIcvMjHlGaOHChdi2bZvpvlmz3KlTp5pG8TsR5+BkrATAvxa7d++OS7sFxMogy3zvjISN6ukxis9xw0tCpA4SA4Q06o9dbyzU+uj6fG2RhyfvwaItESyDlbKwgHnVRJFxePEhabRkwTL6rGp6Ri5kmZkx77NjY2PCn9fPckVrM8h0ceThxD5UeNdix44dzNeNvkOy3zuz32ZTU5NhfI7TXhIitZAYIGKIFj0pKCjgunpZMxGRh6dZiWAZrPRZUOnt7eVWd9OPUV12KCoqipuFeu0haeQ65s0medX0fD6fK7NrnqtfZmlIJJbEqTgOp+JWZK4FYK3MttE2Zr9No/N02ktCpBYSA0QMEWOl/ti7u7u5n2HNRMwenrwHC5BYQtUMK3XbVXjV3fQPudtuuy227BAKhdDZ2Yk1a9Zwz8VuMKJVzFzHstdKdSE7PbvmVfUrLi6W3jcLRVHQ1tYW53r3ShyH6LUwa8ds1TjbETZOekmI1EJigIhhVPSktrY27mFkZYZihv7Bsnnz5rhKhaWlpairqzN94NidgbOqu2mPGQqFEuIPhoaGEAqFYh4Cbb/5HTt22ApGtHsuvNf9fr9hTAermp42kM/ug19kH/qANStGh7dk5GYch+xYRa5FbW2t6X5SYZxTkdpKOA+JASIOXtGT/Pz8uB88axYC2HcTqg+WdevWJYyjv78f/f39XGOqfQDLVBjUo+Z68x5yvNTHwcHBuOUCXoGbZAYTmrmOjWaTfr/1anqymM1qrWZnmC0ZmcVxWBEgdjNJ7LrfZY1zpqbAEnKQGCDikFl31M5+AXM3piiKohgacpYxtRowyEJfy15PUVERs0JjUVFR7N92jZBTiBgWo9lkMmd9Rm2SrWZnmC2DGHmRrBh1pzJJkjXDz5QUWMI+JAaIOGRnJU4ZC+3sRGQdW2tM7QQM1tTUID8/Hxs2bIh73egBHgwG0dnZGbdUoA8itGOEWNiZvYkYFtH76PYskjUOO9kZRtfZ6Htt1ag7mUnithAzCh51StgT6QOJASIOJ8rMysJqimKG9iEvGwRXWlqKk046KXZuvGBIowf4mjVrmNkEZmVlAfmlFF7qn1Nr0qIGXnYW6ZRwsJOd0dvbm/BaWVkZamtrmWMyK5FsZtR59z2V6Xa8+8D73bS3tzPThInMhsQAEcOJMrOy8Gq9G635642pyIPWSNzINnJRCQaDcd4AkbKyPCOkRXs8AMzZm1OuXVEDLztTdtL9zItP6e3tNV3vZ3mMePdAZKlJZllBJZXpdkb3wex34/VCWYSzkBggAKSuah5vdqJvSKR+lpcLzWvlC5g3WTJaGrFjLLXljUVnx/rjlZaWmm5j9T7x7nlBQQF2794N4KM4EF7b36eeegrNzc1C+7XzXeIVQTLap4zLXmSpySg9lLe9nU6fdjFbBmB5TfSILG9QAGJmQGKAAJC6qnlGs3K9a1tmHJWVlSgpKRF+QMk0cmEZIKM676LeFdbx9CVpeVi5T21tbczXtfETqruYV4RIUZSEBlZufJes7FPG42PWQ8OsV4XR/U8VZssAIph5DygAMXPITfUACG+Qqqp56qxci6xblbfUEIlEEA6H4xrUKIqC7u5uZtMav9+Pqqqq2LGNDJAeJ64f73gi3gErAYmiQqOjowPTpk3jvq8ftxvfJSv7NPputba2oqWlBevXr0dLSwsGBgaY+ygrK0NBQUHCklVHR0fcd8hrVSedOLbZ75AnlnkNoQhvQ54BAgC/ah4gXwFQFrtpVDwjqp3hqkZBZhYj84C3mxtudLy5c+caGm4r1Q1lgy5FO9mpY5C9FmYNr3j7BIy/n6IeH1acilm1Ta1Xwuo5u52ZYbR8xkJmWcuLPTgI65AYIGI0NDRg7969sQei1i0KuBs9bieNSrTbIus1ozVn2Qe8XVHDO97OnTuZn587dy5mzZplqbqh7KxRtJOdisy1EGl4xdpnb29vXIVK3rnrv1uicSrqNqKi0M45u+Ve19YCMVsaCAQCUvEtXvSGENbJiUaj0VQPgvAGiqLEPVxZNDc3uxY9bkdMWC06pK4JGx0z2QFS+uM9/PDD6OrqSvjcCSecgO3btye8zrtHekSvWU1NDerr67njs4PRd87oPHjbiQTs8bY1Op7+WumviQxmx3fr+8Y6h/LycoTDYQwMDMSJPNHfr5PXhUgt5BkgYsgW+1FxInrcrpjQzsoikUhCESEe2tmS6MzSbfTHmzdvHlMMTJ48mbm9qJuWNdPW3oN58+bhrLPOYrrstQWf7Bguo+/cli1bYscT3c6seqS6P1mXvpMVAY3c6/p74KTHwOgc1q9fH/dZ0d8vNSrKHEgMEDFE3HsjIyOOR487lYqmNVLhcFjaU+DVvGpeRPqxxx7LfF3GTau9Zn6/XC8CJ7xBRmMNhUIIhULM/RptJ3IfrRgxp0Qhb+yRSMT19F7WOdj9/SZbLBPuQNkERAxW9LWe9vZ2tLS0oLW1Nfaa3bVDmah9URoaGtDc3Mzt9LZo0SLHj+kk2qwHo86C+vtVVlaG4eFhbsaEGfqMCqPxORFJLvKdY+3XbDuR+yh6rqIYZaroj8vKcuCJPta5iB5LBCNx4tQxCO9DngEiDnXGtGXLFmYzHhXtjMXv9ydU2ysqKmI+ZFluZbcCkdSxsVrxlpeXM13vXgh+Ys24zToLtrW1ob+/H319fXEFguy4mbWllfWBZU5GkmuD3N577z1s27ZNaL8NDQ0oKChgLgkl+z7Kekl4WQ4s9OfidPAha9mkqKgoIRuH6gdkNiQGiATUh66RGAA+ekArihInBIAj7Xy1ywmKosQMloq2MJDdtDwjeC5hN49pFd6Mu7m52dCtzUs9tOpm5gUXlpaWoq6uznEBpwobRVGYYoC332AwmLAklKz7qBVLVtz7eve6SCyDW5VCzWJu1GMA/EqgRHpDYoBgIpKjrC0TzEIVCzzD0tPTE4tgLiwsjHvP6SQX1rqmF4OfjK6l1qUt0+VRdrZuVJq3v78f/f39ht4KOyQzuM/t7BU7XhLeuNzM7Vd/I7y6CnoxT96CzILEAMFF+2DSpx5pH9BGs0TR9sJ6z4Kd2Y7MQ94LwU/a8YrMuGW7PMrO1kXW20W8FVZxOriP9X2w42oX/U7b9ZLI7NPJZRFe50W998mrAbeENUgMEIaoD6aqqipmQRb1M7zZHG+WIYKV2U4qa6VbmWnKxAeox5Dp8mhlti4T+OlkAJ4Wp0Qar/2zHVe7iFhiufedEE1WPCcy8Dwe8+bNw9tvv53wOlUbzBxIDBDCGD2gebM5N+rR80hV50XAmgixEh8gUj2PFfAng8gSEeB8kJ7TxXaMuvaxEDVsvPPmlfJ1WqC6tbzF83iUlJRwezd4IeCWcAYSA4RjsMQCy7DMmTMHU6dOjXvATJkyBfv27Yv9XVhYKP2QS1WtdKsiRDQ+QItMl0c76APKent749zETgfpueHRkU0TFTVsvNk5q/KhWwLVjeUt3vXiCQEvBNwSzkFigHAd1bCoAUjvvfce3nvvvVg2ASt6eWhoKKG4kRlG+dJuYlWE2OnEl4zoea3BCQaDrpXJdctgGtVnYKWbyhxLdHaeTs18ZGb5tbW1WLZsmYujIZINiQEiaegDkHp6emKubRZGD0yWYeK5t0VK1NrBalCXVcOeqiwIt4It3TKYRtdXrc9g5xqKXI9MbeZTVlaW6iEQDkNigEgKRg98swem3vAbuZR5hWjcjh0oLS215EaXNezaa6F2EUx33DSYRtc3GZkkyfTk2EV0WcWr4yfsQWKASAq8B/vIyIhh0SFWGp0+al5v6I3Kujr9ENOPTy3KI3McUaOUykwJN3HbYKY6fdSL9SxYiIqv8vJyl0dCpAISA0RS8Pv9TEPe3t4e61/f3NycUJ6VlUbHQmvok+WaZY2vv78fdXV1jh6Hd6xMyvNOF4NplVQLEhF4v1E9Xox3IOxDjYqIpNDa2mr4kFENnTaKXiYaXGvo1ZmmFjdcm7zxaXsDuH0srzRWcgK1ngUZmtSg/41OmzaN+bl0j3cg2JBngHAd0Ypt+hkH76Gjn70EAoEEA+LmTFNbk55Fe3s7xsbGHHXhZ2ogGuENWL/R3bt3J9QYoHiBzIXEAOE6orNXvWHjrSXX19cD+GjJYOvWrZg6dWqC8XXDNStSkx6Id+E7kZLHcuGm+4PZrVRFQhz1HoyMjDDfr6ysxBlnnEH3KQsgMUC4jsjslWfYeK1ezYII3UDUw6ESDofR29vrSNCf3oVbWVkZE0XpSKYGQ9oh2eJIRNg6XcyK8C4kBgjX4c3wy8vLhR5++odRqgq5yK7Py7a25RkDXiBldXV1Wj6kMz0Y0grJFkeywpbIfEgMEEmBt4Zv5eHv5vq50exMZv81NTVSKY5GxsAoUDEd3bfpVJUvGSRDHOm/16LCNlvvSTZCYoBIGk65G93KSzebnYk28AGMc7H1osLMGPBESHt7O3esXoZ3PgMDAxlTSEkGt8XRunXr4paY1M6NIlCAavZAqYVEWtLQ0IDm5masXLkSzc3NttfPeQZZURTD49bW1jL3pz7IRVIczdIGWfvRwxqrV1GDIfX09PSkzTk4iZueLr0QAD5K4zX7TqV7gCohB3kGiLRFX49A9MHFWgqQmZ3pPRzaGbqK+iAXSXEUMQba/YyMjDCPmU4u3ZKSEmbdiXRd+rCDW54uVqCtCuv7XllZiRkzZgA40nsgW64/cQQSA0TaYiXoireNm82GzJZHRI2Buh9FUQwFSDqQSUsfTmQBWKmLYXZco7gAVnBrT08PmpubSQRkKSQGiLTEStCV2TZWZ2dOFDiS2Uc6Nb/hIRJ/kax0UbNrbvQZJ7MAZGJqWD07mpqa4sbMqx0QCASS2r+DSA9IDBBpiZWgK7Nt7Bh1J4IjZfaRCbX8U730IWLIjT6TqhRJo54dTU1NhvUDAoEAVq1axY3NSCfvEuEsJAaItMSKW19km3QqsJJOY+WRqqUPEUNu9hmv1bvo6enBggULmEKgtrY2Lg4gE7xLhLNQNgGRlsg2I1JdvfoodnoAeoNkNZdSEWn8ZPaZVPWLMNr/9u3bma/n5+cnFLKaPXs2GhsbHcvIIdIb8gwQaYuoq5y1vlpSUpK27vVMJZlLHyKG3GvNofSxCyxX/6RJk5jbasfMWvrIxvoORDwkBoiMQ/vQBMBcX03XUr6ZTrKWPkSzQIw+k8xlAr0B5+3/5JNPxuTJk7ljplLQBA8SA0TawprhABB6aGZq1DR1AhRHxBNh9JlkeQ5YBpzlFVCNvt/v546ZSkETPEgMECnFqvHizXBYn2ORiVHT1AlQHhFPBO8zyQrCM+sjoAYHAkB3dzez06D6O4tEIsx9ZOLvgZCDxACRMuwYL9kOgloyKWhQ+5An92/ySUacg5mhzs/PN2yVrf+dFRYWYmhoKPZ3Jv0eCOuQGCBSgt21S9mZTG1tLfLz8zPKfS7Sj57cv+7jdpyDWYEmIyGo/lvL0NAQGhsb4fP5Mur3QNiDxACREuyuXfJctHv27GHWY3eq1ro+ODFV6/Oi/ejJ/Zv+aNMAe3t70d/fH3vPrFU2D5/PRxkERBwkBoiU4ETwlZGLVisIZNygMqVntSRjfV47tr6+PtPPk/s3/WEtpdXV1ZmmGALWCnAR2QuJASIlOBV8xXLRNjU1obq6WnrWLlt6Vovb6/OiqWWLFi3CggULyP2bARgtpWln9Wa/Jf17arAhQWghMUCkDDeDr2TXca2WntXi1vq8aGoZcCTPXB0LIN7W2ez4Tt+jVKZAsor3eDEdU2Ypzei3pL7X1taG/v5+9PX1oa+vj7JNiDhIDBApxSv19c0evCJuVbdcr7yxlZaWJqwfG0WVW8GNdMVUpkCaRdZ7yUDKLqUZ/ZaGh4fjvisAZZsQ8VBvAoKA+YOXVTtfi5vr87yx1dXVobm5OVZbvry8nOnd4HkRFEVBd3d37H3W3zL7E8GpferHavXYWiFgdSxu4VS/htbWVmzYsIH5np0UXSKzIM8AQUAshkHvigWSk01gNjb1/93d3cztWW5lkRny7NmzhfcnihMV8Kx6FkSCLmXH4jba71wkEoHP54OiKMyCQqzvoVmsCwUSEiokBgjiX4jEMOhdsckyGiwhoq02B4i7lUVnyI2NjUL7k8FuFonV+hQiNRlkx+IWeuPu9/u5yz9mwsho5k/ZJoQWEgMEocErMQws1LHxDIBohoaoa9jn8zlebtduFokVz4JoTQbZsbgB695WVFQwBVBBQYGpMOIJm8bGRgSDQYdHT6QzJAYIIo0wmxmLeDdEZ74FBQWoqqpyPOPDThYJr7Y+73VAbl28vLxc+LNOw7u3eXnsx/Tg4CDzda0w4okvEgKEHhIDBJFGiMyMzbwbLANRVFQUZ1z0MQlOz5at7pNXbY/3OiDn9k9lvIBsMF9RURFCoVDC6/rzTUb/BCL9ITFAEGmEE5UbteVttfXpvZpvr8XK+bPETyAQwNatW6X24za8Y4+Ojia8FggE4PP5UFlZKVRt08vLX4Q3yIlGo9FUD4IgCHH068o1NTWor6+3tK2X8upFsXr+WrGjD8iT2Y+b6M9NX0uCRWVlJUpKSjwt4gjvQ2KAIFzGKxX8FEVBS0tLwuvNzc1pZ0TsXFOvXwf13AYGBphNt1h4ZexE+kLLBAThMEYzUKdm4qJuX0VRTPPrvZRXL4odt7cTtQ7cRB3D+vXrhbfxytiJ9IXEAEEYIDsDNctnT2YJWNHc+lTn1cvghJfFSkZCsmlra5P6fDrdQ8KbkBggCA6y6+ui+eyyszirSwIiY9EGnHllOYOHzP0wOq6VjIRkoigKN05g6dKlGB8fd7T2A0EAJAYIgomVSneiqWEyszirAX9GY6mtrUV+fn6coVy3bl3c+rTXGhKJ3A9VAPT09GBgYIB7XJ4HQL0vbmZViOzb6N5t2rQJS5YsQWNjIwYHB1FUVEQ1AwhHIDFAEAysrCuLGHmZWZzV0rtmYykrK4vbXi8EZI7Dw87YWZjdD6MlEe1xeZ9T74ub2Rai+zb7Hmn3EQqFEA6H0y4jhPAe1LWQyFqMOt/ZyWfXUlNTE9dZUCZ1zcgAGqHOPisrKxPe04sRRVG4Eet2OtpZHTsPo/shsiQSDoe5n2tsbER9fb2jXRrtdID0+/0oLCwUPpaXOi0S6Qt5BoisxGyWZrWGfkVFRax87LRp02Lr0FVVVdJjtCJI9OdVWVmJGTNmAEj0CADGxjmVDYn0GN0PXrdG/XF556reI6eyDFjfLZkOkIqiJDSOMoOyCQi7kBggsg5RF7ZsGVcjV7UVd7OsIGGdV09Pj2EOOs84BwKBlDYkUtGmRlZUVCTcD0VRMDIyYrgPs3NRr4GMgOGt/fO+W7wOkCMjIwktia14TyibgLALiQEi65CZAcrk87uRUigjSKzMbFlGu6SkBMXFxQlGSha7NfH14qq9vR2lpaWoq6szjRNQKS0txapVqwA4J1CMvEpG3gf9sdVzam9vj9sHz7DX1tairKwsoXYFZRMQTkBigMg6nHZhA2KzObM2u3Yj2K2el9Zoq1Xv1Gh8uwF0VosD8cRVf38/+vv7E2ryq9TW1mJwcDA2/v7+frS2tsbOwUig8Ioz9fX1xWUsGHmVjO6B2gGyr68P7e3t3H3wRMuyZcsAHLmm1HiIcBoSA0TW4dQMUYuIkOB9xmimKRPdbue8eFXvnCiSZCR0FEVBZ2cnDhw4gBNOOCGWJmcmrozK9GrTCoHEc3CjeuGWLVti+2bdAwDo7u42/J5oRYeZV4UaDxFOQ2KAyEqcbuvKMgJaeEbZaKap/pv1Hm+8ds7LjTK9MkJn27Zt6OzsxJo1axxfAxc5h7KysoQZu/q6Cm9coVAIoVAodn4FBQWxOgDhcDiuF4LotSSDTyQTEgNE1uL0w1ZviAFYLjBjNDM2M2xWz8vp5RNZoQMAQ0NDCIVCCAaDhuIKALN9L8+gi5yDiGfFTPR1dHRg7969sXGFQqGEz/DSALWigyCSDYkBgnAQvSFmucW1AoFnpCKRCHbv3s18z63IcSczAMLhMDfK32wJoLu7Gz6fDxUVFXGGVUtZWRmqq6tRXV2dkF2gb/srcw4inhX1Mxs2bMDw8HDC+yKdBu2MkSDcgMQAQbiISAdDvQGeNWsWNmzYwNyf20bD6QwAFmZi5q233sJbb71l+Jm+vj709fUZLjvMnj0bp512GoLBoFSApqhnhSUERKmrq0NFRQWVFCY8A4kBgnAJ0Q6GDQ0NcTPgnTt3JnxWTStLxuzR6QwALVoxY7YMIILRssPw8DA2bNiAzs7OuCI+TpQY5nk3VO+EEWVlZdi8eXPcUgKVFCZSDYkBgnAB0a6BajqbmWs5Pz/f825knoFcuHAh5syZkyBmVC9Ea2urqSfA7LivvPIK9319NT8nMiR43o0ZM2ZwxYAqFFgpjMlsbU0QLKg3AUFoMOpXIINoFbmenh5ufruWdKgwxxvjtm3b0N7ejt7e3oT3/H6/7RlxJBLhtvzlwbs/ovef1YciEAgYijqzfdrpBUEQdiHPAEH8Cyc71oka79HRUWb0u5Z0CS4TibRnzX6NtqupqUF5eTk37gIAU2SYwbo/svdfH18RDoexdetW6bEYjYkgkgWJAYKA8y13zQyjKLW1tbHKc+mAaiC3bNnCTKvTpkVqg/q0hjUSicDn88UF+2nvgf6amnkFJk+ejP3798f+ZokrK/dfJigxEAgYCoV0EXxE5kJigCDgTsEdtfgMLzNABK/mnhsZQvVvlhhQZ7+8WbjZtebdJ32qnhZVCGj7GojuV31dn77Y1tYWdzxeZkhZWRlqa2vh9/sxderUuPcCgQCKi4uppDDhCUgMEATc6VcAAMFgEOFwWMhDwCqi40UjIeJON6pZYMcLw7sfdXV1qKurSzDSWvr7+1FXVye134GBgbgSzYWFhcz2wtrMEF5qptNVLwnCSUgMEATc6Veg0tDQgLy8PNPYAFYRHRZONDWSRT1mJBIRNuQ842fHC2N2n1avXg1FUYSWKcz2y3Lts4SAitpbwCg1k0oME16FxABB/As3Z268MrlawuEwqqqqDI8rE+TmlGgQKSRkZGT1r9v1wog08QGMlylE9isbENje3o6xsTGqF0CkJSQGCEKDWzM3kYBCM2Mo4153KjNCtF6CzHKKE14Ys/vEOkZpaalj1Qd5dHR0IC8vL2kFogjCKXKi0Wg01YMgiGxBna0PDAwkxAfU19cbbtvd3Z3QYhgAVq5ciaqqqrhjaLvkqTQ3N0sbKN4xtYiMnYXWcwGYN3Wywh//+Efs2rUr9ndhYSHWrFkjvL1eVPFiBlg4UemQIJIFeQYIIomoM8+qqiqh+AAtou51JzMjeMdsbGxMSP+TRb0WTtZ30BIKheKEABDfFVEE1pKENn7CKFOEqgoS6QRVICSIFKGKAgCWq96x3OtOZkbwjhkMBk3jG0TgLX2wroVsdcjBwUGp13mo90lb86CqqirWZtkIqipIpAvkGSAIizgRoGe36p1oZLydzAg3AytFvRhWvAdFRUXMIMKioiKLo5WHqgoS6QKJAYKwgF3Xttqwxkq+vUiQm4gBl23rC3xkvJ0SBCJeDKt1CYLBYELHQifbBZsFV3q1TgRBsCAxQBAm6I2m3dLFZql6dqoeajESDbJixq11fREvhp0YiDVr1iAUCmFwcNBRIWA0rmAwiMWLF5MQINIKEgMEYQDLCM6ePZv5WRHjJJKq57ZrORQKSYkZp/s26GH1JVAUJbZvuzEQwWDQURFgdnwSAkQ6QgGEBMGBZwQjkQjz8wUFBaZBbmbtit12Lbe2tnIj4M3q84u+LoN6vQBgeHgYGzZswPr169HS0oLW1lYA7CBGwFq3QicRDegkiHSAPANE1mK2Zs4zdj6fj9mQZvPmzXG1A2Rc6QsXLsTSpUuFPAtWA/nMvBKyM3C7Hgyz5RKt96GiosJV74RVqN8AkSmQGCCyEpE1cCMjWFVVhYqKilhjHNaMn2WseGWJRYSA3XV7o5m80YzWjb4NopUN1TFv2bKF+77bBthMgFG/ASITIDFAZB2ia+AiRpDXIU9Fb6ysGlYn1u2NCgiZrak7PQMWXWLQdw3U43Z8hVuBkwThNUgMEFmHSHS6OhusqKjgGkERg8YyVlYMqxNVBXlCRDS4zskZsIgRZ3UN1OL2+rzbgZME4SVIDBBZh9kauOhs0MygmbneZQyKU+v2Xlnj5gmT8vJy066BRql7VmIqeNs4WdaZILwOiQEi6zBy1cvMBln7CQQCKC4udtzQOrlu75U1bp4wMRsbTwhYcekbbeNW4CRBeBESA0RWwjNEsrNBdT9qAKGbrWu9Mqt3EiNhIiOArLj0zbZxI3CSILwKiQEia2EZIiuzwd7e3pjBaG9vR2VlJZqampwbqAae8XSiT4IXERVAVlz6IttkogAjCBYkBghCg+xskDW7VGsNuCEIWEY/0yPeVQGkFihiGWUrIo5XPEr/uleWVQjCTUgMEIQOmdkgb3bZ09OD6upqR40Iy+g7WYzHy94FM8FjxaXv8/mYr+/evZsrOggiUyExQBAMRGeDRjNPJ6POeevbeXnsn7Dssb3sXRCNB5B16fPunbYolJeuA0G4CfUmIAgb+P1+VFZWMt9zMupctg+AzLF5xpbXX8FJzHo5AHK9Efx+P6qqquKWFUKhEPMYRvdOJVnXgSBSDXkGCMImamyAti+B01HnPONeVlaGsbExWxHvZsbWraUDu/UcWK+rSx0DAwNx94N3jJKSEubntFBdASIbIDFAEA7Q1NSE6upq1wyn0Zq42sjH6rF5xlZfCthJl7ndeg4swWPW+Eg9Rl5eXiwFVMSDQnUFiGwgJxqNRlM9CIIgxHAryE9vSHmlgJubmx05bnd3N7PnwMqVK1FVVcXcxujcFUVBS0uL1BhUcWMkImpqalBfXy+1X4JIR8gzQBCSpDLq3q00N33wHa8UsFMucyupgEbnLhtTAXzkidCfu7o/yiYgsgkSAwQhgZej7u0iIjTsuMz1IsrJ6n68mgFmqOJGf+4kAohsg8QAQQiSTV3snDbWPBHlRHU/nptf7RMRiUSwe/fuuJRBFYoHIIgjkBggCEGc6mLn5eI+Wpwy1iI9AKzC2jcANDY2JrRmZmVdAKACQwQBEgMEIQxvFjkyMgJFUYSMSbotMzgRo+BmK2Devnt7exPEgF7c9Pb2xgUdev1eEISbUNEhghBEdZ3raW9vR0tLC1pbWw23t1vcR6RAT7KQGYubrYB5++jv72eOTS1KBCBlhZYIwouQZ4AgJNC2LNavQZvFD9iZIa9bty6uOI5bs1iRJQxZ74abrYD9fj9KS0vR39+f8J7djoUEkU2QGCAISfx+vyVjYnWGrBcCgDuBiyJG3moQpZutgOvq6phiwOi6uumtIIh0hJYJiKzAaRe71Tx5/TKD2QxZURRuuVwrufVGxxFxm8v0CdCj7RvgNKWlpXF/m11XK/eCIDIZ8gwQGY8bQXtWXd+yM2QjI5uMRkh6T4eXZtSKoqCtrS3OK1BaWoq6ujoho+6mt4Ig0g0SA0RG42ZtAKvGRCZCn2dkA4FAUhoh6V9niSDgSPR+Mo0pr7ZAf38/6urqhPfjVkVHgkg3aJmAyGisurVFlxXcdH2r+9e7swOBAFatWuX6cXiejoqKioTXkhmJz6stoKLeWy9lXxCE1yHPAJHRWHFre60WQLLc2aLHSXUkvpmQKygo8Nw9JAivQ54BIqPh1Qbo7e1lft5uLQC3cNsDIXOcVMcNGB1HrSroxXtIEF6GxACR8ci4te1Ey2cLqY7EZx1/3rx5qK2tRXl5Od1DgrAALRMQGY+MWzsZs16zwj7q+5FIBD6fz5OR7qmOxNcef2BgAD09PXj77bfR3t6OyspK5jZUQ4Ag+JAYIDIeGQPvZrU8wDgegZUqx/qcV0h1JL567PXr18e93tPTg8rKyrj6DFRDgCCMITFAZDwiBl5RFPT19QE4sqzgxqzXKM2xt7fXMEI+U1sl24Xn9SkpKUF1dTXVECAIQUgMEFmBkVtbP1tXXc0lJSWOjoFnuPr6+gyFgHZ7MmrxGHl9Uu25IIh0gsQAkTWwjAMvZ72npyfmZnbKRW93zZrWvBNxe1mHILIFEgNEViMSYc5z0Yt0+BNh2rRppp+ZM2cOGTgOqQ5mJIhMgMQAkdWIzrb1LnorRW14wmP37t2mx6+urhYaZ7ZCSwIEYQ+qM0BkNbyiRHq0osFqYSKrbv6ioiIEg0FL22YTVH6YIKxDngEi62loaEBubi42bdrEfF+/Bm21HC9vfbusrAzt7e0Jn1+4cCFOOOEEEgIctMs0+mwML6ZiEoSXITFAEAD279/PfH3hwoWor6+Pe82sboFRLAFvfVsvEgKBAIqLiylokAOva6EKpWIShBwkBggCR1zxoVAo4fUTTjgh4TWjCHaRWALW+ra+ot7WrVuxdetW7j6yGbOuhSqUikkQ4pAYIAgAwWAQnZ2dGBoair1mtFbPmuEbFRUSMUq8ino0y/0IRVGwYcMGoc+SV4UgxCExQBD/YsGCBXFiYP78+Yaf18/w7bb2VRQFW7ZssbWPTMZsaUAL1RogCDlIDBAEjEsFixoVO02OzAxdts9yzZYGysrKUFtbS7UGCMIilFpIEHCmdbHV1r5mho5mucADDzxg+H5tbS38fj+qqqqy/loRhBXIM0AQcK51sZVqeDzBEQwGsXjx4qw3bvfccw8++OAD7vtlZWVZf40Iwi7kGSAIWJ/V8/YlM0PlCQ4SAke8JgMDA4afqa2tTdJoCCJzIc8AQfyLVNW4p2Y7ibUZ1L9HRkYMt8u260QQbpETjUajqR4EQRDONT5KB4yqBxYWFsZldbCYOnUqLrzwwoy/TgSRLEgMEASRVGRSBFmUlpZi9erVDo6IIAhaJiAIImmIVg/UU1tbi/z8/KzwmhBEKiAxQBBE0pBJ1dRCGQME4S6UTUAQaUCmtOcVSdUsLCyM+5uCBAnCfcgzQBAeR6T5Ubrg9/sTAgSLiopw9tlnM7MJaFmAIJIDiQGC8DBOlElOFiIGXFGUhEyBwcFBAEBVVVXsNVZnR4Ig3IPEAEF4GLvNj5KFqPciXc6HILINihkgCA/DW2OPRCKeiSHgeS9YY3Oq7DNBEM5CngGC8DC9vb0JrxUWFmLDhg2xv1MdQyAz26dqiwThTUgMEIRH4eXk69fcUx1DIDvbT1XZZ4Ig+JAYIAiPIpOTn6o1d0VR0NbWlvC6fravDS4EQEKAIDwGiQGC8Cgy6+hurLmbZQcYlRUuLy8X+lyqlzgIgjgCiQGC8Cis9XUWgUDA8gybZ/DNsgPMygqrngqzz6V6iYMgiCOQGCAID6NdXx8ZGUF7e3vCZ4qLiw33wTP469atQ09PT+zvJUuWoKKiAp2dnejq6orbh95os5YGtGiXA8ygtEKCSD0kBgjC46gFeBRFYYoBoyUC3gxfLwSAIwbfaBa/bt06NDU1oa+vD/39/dzPaeMFRJYvKK2QIFIPiQGCSBNk0/J4+f8FBQUJQkCE0dFRtLS0cN+fM2cOVqxYETcev9+PkpISDAwMMLehtEKC8AYkBggijZBJy+O56NXyv06jFwIqF110Ef74xz9i165dsdemT5+Oz3zmMyQECMIjkBggiDRDtG4/z/1eVFSEUCjk6JjMZvhXXXUVQqEQBgcHMXnyZMycOdPR4xMEYQ8SAwSRofCWFYLBIMLhsGmWggi1tbUoKysTEifqcTdt2hR7jVILCcIb5ESj0WiqB0EQhHvwsgnU13lZCrW1tRgdHeXGF9TU1KC+vl5qHKyYg+bmZlouIIgUQ54BgshweMsKZlkK6oy/urrakeqB1LGQILwLiQGCyHLMshT0YsKq4aaOhQThXWiZgCAIAOblh51AX/dAdqmBIAh3IDFAEERSSYboIAhCDhIDBEEQBJHl5KZ6AARBEARBpBYSAwRBEASR5ZAYIAiCIIgsh8QAQRAEQWQ5JAYIgiAIIsshMUAQBEEQWQ6JAYIgCILIckgMEARBEESWQ2KAIAiCILIcEgMEQRAEkeWQGCAIgiCILIfEAEEQBEFkOSQGCIIgCCLLITFAEARBEFkOiQGCIAiCyHJIDBAEQRBElkNigCAIgiCyHBIDBEEQBJHlkBggCIIgiCyHxABBEARBZDkkBgiCIAgiyyExQBAEQRBZzv8HP9h+m12rlnMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot(query, projected_dataset_embeddings, projected_query_embedding, projected_retrieved_embeddings):\n",
    "    \"\"\"\n",
    "    绘制投影后的数据集、查询、检索结果的散点图.\n",
    "    \n",
    "    Args:\n",
    "        query (str): 查询文本。\n",
    "        projected_dataset_embeddings (np.ndarray): 整个数据集的二维嵌入向量。\n",
    "        projected_query_embedding (np.ndarray): 查询的二维嵌入向量。\n",
    "        projected_retrieved_embeddings (np.ndarray): 检索的二维嵌入向量。\n",
    "    \n",
    "    Returns:\n",
    "        None\n",
    "\n",
    "    \"\"\"\n",
    "    plt.figure()\n",
    "    plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']  # 设置全局字体为微软雅黑，显示中文\n",
    "    plt.scatter(projected_dataset_embeddings[:, 0], projected_dataset_embeddings[:, 1], s=10, color='gray') # 整个数据集的散点图\n",
    "    plt.scatter(projected_query_embedding[:, 0], projected_query_embedding[:, 1], s=150, marker='X', color='r') # 查询的散点图\n",
    "    plt.scatter(projected_retrieved_embeddings[:, 0], projected_retrieved_embeddings[:, 1], s=80, facecolors='none', edgecolors='g') # 检索的散点图\n",
    "\n",
    "    plt.gca().set_aspect('equal', 'datalim')\n",
    "    plt.title(f'{query}')\n",
    "    plt.axis('off')\n",
    "    plt.show()\n",
    "\n",
    "plot(query, projected_dataset_embeddings, projected_query_embedding, projected_retrieved_embeddings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上图中，红色的“×”即为我们的查询（query），绿色圆圈圈住的点为检索结果。\n",
    "\n",
    "注意：这里我们将高维空间压缩到了二维空间，这个可视化结果并不总是完美的。\n",
    "\n",
    "可以看到一些检索结果都围绕在查询四周，但也有一些点离得较远。\n",
    "\n",
    "这些离得远的点就是问题所在，它没有答到点子上。\n",
    "\n",
    "这是因为**我们问的问题很泛，没有提供足够的背景信息，嵌入模型很难理解我们的意图，难以去完成我们内心期待的特定任务**。\n",
    "\n",
    "我们换一个问题试试：一般公共预算收入是多少？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "一般公共预算收入增长5%\n",
      "\n",
      "一般公共预算收入增长8.2%、突破6000亿元\n",
      "\n",
      "压减一般性支出和非紧急非刚性支出23.9亿元\n",
      "\n",
      "“三公”经费减少5%；深化全成本\n",
      "预算绩效管理改革\n",
      "\n",
      "统筹用好政府投资基金\n",
      "\n"
     ]
    }
   ],
   "source": [
    "query   = \"一般公共预算收入是多少？\"\n",
    "results = chroma_collection.query(query_texts=query, n_results=5, include=['documents', 'embeddings'])\n",
    "\n",
    "# 检索结果\n",
    "retrieved_documents = results['documents'][0]\n",
    "\n",
    "# 查看检索结果\n",
    "for document in results['documents'][0]:\n",
    "    print(word_wrap(document))\n",
    "    print('')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到前2个结果与问题较为相关，后面的3个结果的相关性逐渐减弱。\n",
    "\n",
    "接下来进行可视化。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:00<00:00,  1.40it/s]\n",
      "100%|██████████| 5/5 [00:04<00:00,  1.15it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "query_embedding      = embedding_function([query])[0]  # 查询的嵌入\n",
    "retrieved_embeddings = results['embeddings'][0]   # 检索的嵌入\n",
    "\n",
    "# UMAP降维（投影到二维平面）\n",
    "projected_query_embedding      = project_embeddings([query_embedding], umap_transform)\n",
    "projected_retrieved_embeddings = project_embeddings(retrieved_embeddings, umap_transform)\n",
    "\n",
    "# 绘制散点图\n",
    "plot(query, projected_dataset_embeddings, projected_query_embedding, projected_retrieved_embeddings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到一些检索结果和我们的查询离得非常近，但也有一些结果离得非常远。\n",
    "\n",
    "我们再来换一个问题试试。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "城镇新增就业28.1万人\n",
      "\n",
      "实现城镇新增就业不\n",
      "少于26万人\n",
      "\n",
      "城镇调查失业\n",
      "率4.4%\n",
      "\n",
      "城镇调\n",
      "查失业率控制在5%以内\n",
      "\n",
      "持续增加城乡居民收入\n",
      "\n"
     ]
    }
   ],
   "source": [
    "query   = \"城镇新增就业人数多少？\"\n",
    "results = chroma_collection.query(query_texts=query, n_results=5, include=['documents', 'embeddings'])\n",
    "\n",
    "# 检索结果\n",
    "retrieved_documents = results['documents'][0]\n",
    "\n",
    "# 查看检索结果\n",
    "for document in results['documents'][0]:\n",
    "    print(word_wrap(document))\n",
    "    print('')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从检索结果来看，第一个检索结果答到了城镇新增就业人数的具体数字，但后面的几个结果存在不少**干扰项**。\n",
    "\n",
    "干扰项是指和查询主题相似但不包含答案的文本，**如果将这些信息传递给大型语言模型来完成RAG任务，模型往往会被这些信息分散注意力，导致输出次优的结果**。\n",
    "\n",
    "干扰项导致的模型不良行为对于用户和开发者来说都很难诊断和调试。\n",
    "\n",
    "因此，让检索系统更加健壮，返回相关结果而不返回干扰项对于模型来说非常重要。\n",
    "\n",
    "接下来我们将结果进行可视化。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:00<00:00,  1.38it/s]\n",
      "100%|██████████| 5/5 [00:03<00:00,  1.26it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "query_embedding      = embedding_function([query])[0]  # 查询的嵌入\n",
    "retrieved_embeddings = results['embeddings'][0]   # 检索的嵌入\n",
    "\n",
    "# UMAP降维（投影到二维平面）\n",
    "projected_query_embedding      = project_embeddings([query_embedding], umap_transform)\n",
    "projected_retrieved_embeddings = project_embeddings(retrieved_embeddings, umap_transform)\n",
    "\n",
    "# 绘制散点图\n",
    "plot(query, projected_dataset_embeddings, projected_query_embedding, projected_retrieved_embeddings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最后我们来尝试换一个与文档无关的话题：乔丹干了啥？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "过去一年\n",
      "\n",
      "过去一年\n",
      "\n",
      "主要做了以下工作\n",
      "\n",
      "做好新时代北京宗教工作\n",
      "\n",
      "唱好京津“双城记”\n",
      "\n"
     ]
    }
   ],
   "source": [
    "query = \"迈克尔·乔丹最近做了什么？\"\n",
    "results = chroma_collection.query(query_texts=query, n_results=5, include=['documents', 'embeddings'])\n",
    "\n",
    "# 检索结果\n",
    "retrieved_documents = results['documents'][0]\n",
    "\n",
    "# 查看检索结果\n",
    "for document in results['documents'][0]:\n",
    "    print(word_wrap(document))\n",
    "    print('')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "显而易见，乔丹和北京市政府的年度报告没有任何关联，检索结果里也确实没有提到他。\n",
    "\n",
    "但是，如果我们将检索结果用作RAG任务的一部分，LLM的回答将完全基于这些**无关结果**得到，这就容易出现牛头不对马嘴的情况。\n",
    "\n",
    "我们再来看一下投影结果："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:00<00:00,  1.37it/s]\n",
      "100%|██████████| 5/5 [00:03<00:00,  1.26it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "query_embedding      = embedding_function([query])[0]  # 查询的嵌入\n",
    "retrieved_embeddings = results['embeddings'][0]   # 检索的嵌入\n",
    "\n",
    "# UMAP降维（投影到二维平面）\n",
    "projected_query_embedding      = project_embeddings([query_embedding], umap_transform)\n",
    "projected_retrieved_embeddings = project_embeddings(retrieved_embeddings, umap_transform)\n",
    "\n",
    "# 绘制散点图\n",
    "plot(query, projected_dataset_embeddings, projected_query_embedding, projected_retrieved_embeddings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到关于迈克尔·乔丹的结果到处都是，因为查询与我们数据集中的任何数据都毫无关联。\n",
    "\n",
    "因为这是一种几何类型的数据，想象一下所有的数据是一个位于高维空间中的点云。\n",
    "\n",
    "**落在点云内部的查询可能会找到最近邻，它们在点云内部密集而紧密地靠在一起，但是落在点云外部的查询可能会找到来自该点云许多不同部分的最近邻，因此它们更加分散。**\n",
    "\n",
    "而乔丹与我们的文档主题毫不相关，因此这个查询是落在点云外部的查询，故它的查询结果分散在点云四周。\n",
    "\n",
    "接下来你也可以尝试，换一个查询并可视化一下检索结果，来更好地理解简单向量检索的运行机制和缺点。\n",
    "\n",
    "<a name='33'></a>\n",
    "## 3.3 总结\n",
    "\n",
    "在这个实验中，我们学到了：\n",
    "\n",
    "- 如何用`UMAP`将高维Embedding向量投影到二维平面并进行可视化；\n",
    "- **简单的向量检索可能会返回一些无关答案造成干扰**。\n",
    "\n",
    "在下一个实验中，我们将向你展示通过使用一种称为查询扩展的技术来改善LLMs查询质量的技巧。\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "chroma",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
